Features of new Lidar 3D mapping system.
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). 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Autonomous vehicles have already started to appear on the roads across the globe. Clearly, as the AV market expands, transportation professionals and researchers must address an array of challenges before AV soon becomes a reality. Several government and industry entities have deployed demonstrations and field tests of the technology. Centres for testing and education, products, and standards for AV have also been established. Currently, researchers, scientists, and engineers are investing significant resources to develop supporting technologies.
The self-driving system involves three main elements: orientation, visibility, and decision, as shown in Figure 1. For each element, certain functions are performed with the aid of one or a combination of technologies. For AV orientation, the position of the vehicle is determined mainly using Global Navigation Satellite System (GNSS). To increase reliability and accuracy, this technology is supplemented with other data gathered from specific perception technologies, such as cameras and internal measurement devices (tachometers, altimeters, and gyroscopes).
Elements of autonomous vehicles and their functions and technologies.
The visibility of AV, sometimes referred to as perception, involves infrastructure detection (e.g. sight obstacles, road markings, and traffic control devices) and traffic detection (other vehicles, pedestrians, and cyclists). High-precision (HP) maps help the AV not only perceive the environment in its vicinity, but also plan for the turns and intersection far beyond the sensors’ horizons. The other main technology that represents the ‘eye’ of the AV is the Light Detection and Ranging (Lidar). Other technologies used for infrastructure visibility include video cameras which can detect traffic signals, read road signs, keep track of other vehicles, and record the presence of pedestrians and other obstacles. Radar sensors can determine the positions of other nearby vehicles, while ultrasonic sensors (normally mounted on wheels) can measure the position of close objects (e.g. curbs). Lidar sensors can also be used to identify road features, like lane markings. Three primary sensors (camera, radar, and Lidar) work together to provide AV with visuals of its 3D environment and help detect the speed and distance of nearby objects. The visibility information is essential for the safe operation of the AV. For example, the information can be used to ensure that adequate sight distance (SD) is available and if not to take appropriate actions.
The decision of AV is based on the data generated by the AV sensors and HP maps. Examples of the decisions that are made by AV include routing, changing lanes, overtaking vehicles, stopping at traffic lights, and turning at an intersection. The decision is made by an on-board centralized artificial intelligence (AI) computer that is linked with cloud data and include the needed algorithms and software.
For the AV to function effectively, it must communicate with the environment as part of a continuously updating, dynamic urban map [3]. This involves vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Internet of Things (IoT) technology provides telematics data about the condition and performance of AV, such as real-time location and speed, idling duration, fuel consumption, and the condition of its drivetrain.
This chapter mainly focuses on the visibility of AV and briefly addresses the decision element. Section 2 presents important visibility fundamentals related to highway geometric design and mathematical tools. Section 3 presents details on the Lidar system, which represents the ‘eye’ of the AV. This section also introduces a new Lidar system that has great potential for high precision mapping for autonomous vehicles. Section 4 describes the methodologies related to infrastructure visibility and traffic visibility. Section 5 presents some implementation aspects and Section 6 presents the conclusions.
In the geometric design guides by American Association of State Highways and Transportation Officials and Transportation Association of Canada [8, 9, 10], the available sight distance for human-driven vehicles is measured from the driver’s height above the pavement surface. For autonomous vehicles, the driver’s eye is the Lidar. The ability of the AV to ‘see’ ahead is critical for the safe and efficient operation. Sufficient sight distance must be provided to allow the AV to stop, avoid obstacles on the roadway surface, overtake slow vehicles on two-lane highways, and make safe turns/crossings at intersections. For human-driven vehicles, the required sight distance is based on driver’s perception-reaction time (PRT), vehicle speed, and other factors. A value of 2.5 s is used for human-driven vehicles and a value of 0.5 s has been assumed for AV in the literature [11]. This smaller reaction time will result in shorter required sight distances for AV.
For autonomous vehicles, four basic types of SD are defined as follows:
Stopping Sight Distance (SSD): This is the distance traveled by the AV during system reaction time and the braking distance from the operating speed to stop.
Passing Sight Distance (PSD): This is the distance, including system reaction time, required by the AV on rural two-lane roads to allow the vehicle to pass a slower vehicle by using the opposing lane.
Decision Sight Distance (DSD): This is the distance that allows the AV to maneuver or change its operating speed or stop to avoid an obstacle on the roadway surface.
Intersection Sight Distance (ISD): This is the distance along a cross road with a right of way that must be clearly visible to the crossing AV so that it can decide and complete the maneuver without conflicting with the cross-road vehicles.
Since the AV response time is less than the driver’s PRT, the required SD for autonomous vehicles would be shorter than that for human-driven vehicles. The impact of AV on highway geometric design has been explored in a preliminary manner by making simple assumptions regarding system reaction time and Lidar field of view [11]. The study focused on SSD, DSD, and vertical curves.
The required SD for autonomous vehicles, Lidar height, and object height influence the design and evaluation of highway vertical, horizontal, and 3D alignments, as shown in Figure 2. The Lidar height above the pavement surface is critical in determining the available sight distance. For crest vertical curves (Figure 2a), since the required SD for autonomous vehicles are shorter, the required Lidar height for safe operation, hL, would be somewhat less than the design driver’s eye height, h1. Thus, by placing the Lidar at or above hL, the AV can safely operate on existing highways without the need for modifying their design. For a sag vertical curve with an overpass (Figure 2b), where the truck driver’s eye height controls the traditional curve design, the required Lidar height for safe operation would be somewhat larger than the design driver’s eye height. Thus, by placing the Lidar at or below hL, the AV can safely operate on existing highways. For horizontal curves (Figure 2c), the Lidar height is not important for detecting horizontal obstacles, except when cut slopes are present.
Effect of Lidar height and sight distance on the operation of AV on different highway alignments. Note: A = algebraic difference in grade, C = lateral clearance, hL = Lidar height, h2 = object height, SL = effective Lidar range, L = vertical curve length, R = radius of horizontal curve, S = sight distance. (a) Crest vertical curve (b) sag vertical curve with overpass, (c) horizontal curve and (d) sag vertical curve.
For human-driven vehicles, sag vertical curves (Figure 2d) are designed based on the distance that the vehicle headlamp can illuminate ahead of the vehicle, where the angle of light beam upward from the vehicle plane is considered equal to 1o. What is interesting is that for AV the Lidar height is irrelevant to the operation on sag vertical curves. The reason is that Lidar detection does not require light and therefore an obstacle ahead of the autonomous vehicle can be detected under all light conditions. It is necessary, however, to ensure that the effective Lidar range is greater than the required SD.
Note that the geometry of SD shown in Figure 2 represents individual horizontal or vertical alignments. For 3D alignment (combined horizontal and vertical curves), the AV can directly determine the available SD and compare it with the required SD. The sight line in this case may be obstructed by the pavement surface or by obstacles on the roadside, such as a building or a cut slope.
Quantitative visibility estimation (VE) involves four basic components as shown in Figure 3: (1) sight point, (2) target points (e.g. lane marking, traffic sign, and stalled vehicle), (3) line of sight (LOS) which connects the sight point with target points, and (4) obstacle data that obstruct LOS (e.g. vegetation, barrier, and building). The purpose of VE is to determine whether the obstacles affect the visibility of the targets to the sight point in a mathematical manner.
Basic components of quantitative visibility estimation.
The accuracy of VE is closely associated with the precision of obstacle data. In the past, due to a lack of effective and affordable technique to collect dense and precise geospatial data, the obstacle data were conventionally represented by digital surface and terrain models generated from sparse geospatial points. However, it was proved that neither model could handle objects with complex shapes and may yield inaccurate VE results in some cases [12, 13, 14].
Over the past five years, mobile laser scanning (MLS) data have been recognized as a reliable data source for conducting visibility-related analyses [15, 16, 17, 18]. MLS point clouds enable a very accurate and precise representation of real-world environment. In this case, a number of indoor computerized estimations on MLS point clouds are viable to replace risky and time-consuming outdoor field measurements [16]. In addition, MLS data are also the main data source for manufacturing HP maps which are indispensable to AV. Therefore, the mathematical model for estimating visibility in autonomous driving is built based on MLS data.
The general workflow of VE using MLS data is graphically presented in Figure 4. For a given eye position, both the target points and obstacle points undergo the same procedure (i.e. coordinate transformation and segmentation) and two respective depth maps of the same size are generated. The visibility estimation is achieved through comparing two 3D depth maps. The process of VE is described in detail next.
Workflow of VE using MLS data.
To illustrate, consider VE at a single position. Let
Coordinate transformation: (a) different coordinate systems and (b) visualization of points in different frames (generated with Matlab 2020b; the same for Figures 6,7, and 8-10).
where
To enable a more intuitive and efficient estimation of target visibility, the local Cartesian coordinates are further converted into spherical coordinates as follows,
where
Points in different coordinate frames are visualized in Figure 5b. The sight point marked in figure corresponds to
Based on the preceding assumption, the main task is to segment points in the
Step 1: Grid the original 3D data
where
Step 2: Let
where
In a matrix, each element can be accessed through either its 3D location or the linear index
Step 3: Sort the points by increasing
Due to the high computational simplicity, detecting the jumps of
Figure 11b shows the time performance of the linear index based segmentation procedure on a computer with the processor of Intel®371 Core™ i5–6600, RAM of 16 GB and GPU of Nvida®GTX-960. As
Using the procedure shown in Figure 11a, the target points
Comparison of two depth maps.
Sight obstacle detection: (a) principle and (b) results.
A growing number of diverse end users requires 3D data as many new outdoor and indoor application domains benefits from three dimensional (3D) maps. The new application domains include AV, smart cities, asset management, augmented virtual reality, as-built drawings, and even gaming industry. Active and passive sensors are the main sensor types that can be used for scanning the surrounding environment and generating 3D data. Lidar scanners are the main active sensors, while optical cameras are the main passive sensors. The Lidar sensors are considered the standard sensors used for 3D scanning and mapping. Unlike passive sensors, Lidar scanners do not require an external source of light to illuminate the target. Thus, it can scan day or night under all lighting conditions and with a higher resiliency to scan in adverse weather conditions. The technological advances in Lidar scanners and its miniaturization are rapidly growing and can be deployed for several transportation application domains, such as AV, road furniture mapping, road condition assessment, and 3D visualization.
A Lidar scanner is an active Remote Sensing Sensor (RSS) that uses light as the source of target illumination [22]. The Lidar unit emits a pulsed light beam or a continuous light wave that hits the object and reflects back to the sensor. The precise measurement of the range from the sensor to the target might follow one of two methods. The first method involves the accurate measurement of the Time of Flight, which is the time interval that has elapsed between the emission of a short (but intense) light pulse by the sensor and its return after being reflected from an object to the sensor. Then, the range is given by
where
The precision of the time measurement determines the precision of the range measurement which is given by
where
The precision of the time measurement determines the precision of the range measurement, with the high-end Lidar scanners (surveying scanners) capable of measuring single pulse timing to a 3 picosecond (ps) accuracy and thus achieving a 1 mm range resolution. The range precision is given by
where
Generally, laser scanners survey the surrounding environment by steering the light beam through a mirror or a prism mechanism to cover one direction (e.g. vertical direction). To provide a sequence of profiles around the vertical axis of the laser unit and generate a 3D point cloud of the area around the laser unit, a controlled and measured motion in another direction (e.g. azimuth direction in the static terrestrial laser scanning case) is applied. Nevertheless, if the laser unit is mounted on a moving platform, the controlled and measured motion in the azimuth direction may be substituted by the platform movement.
Beam divergence is yet another factor that affects the 3D point cloud generation [22]. The light beam is collimated when emitted from the laser unit, but as the light beam propagates, the beam radius or diameter increases and the increase is related to the distance the beam travels. Beam divergence is an angular measure that relates this increase to the distance it travels. The divergence affects the footprint that is measured by the beam. Thus, the measured distance represents a wider area on the target and, in turn, will decrease the specificity of the measured distance, as it will miss any position variation within the footprint. The effect is further highlighted in the case of long-range sensors, such as those used in Airborne Laser Scanning (ALS). This explains the narrow beam divergence needed in the sensors used for ALS, which is typically 0.5 mrad or less.
The Lidar sensor can be used in a mobile or static mode. In the mobile mode, the sensor is mounted on a moving platform such as AV or included as part of a 3D mapping system for mobile mapping and 3D map generation. In the static mode, also known as roadside Lidar for traffic monitoring, the sensor is mounted at a fixed location at an intersection to measure real-time traffic volume and speed. In the static mode, the sensor can use power over Ethernet as a versatile way of providing power to the sensor. The sensed data can then be classified using AI and big data algorithms as pedestrian/cyclist/vehicles objects, where a unique ID is then assigned to each classified object. The object can be continuously tracked using the position, direction, and speed. A complete solution that empowers a smart traffic flow management can be designed by integrating the Lidar within a perception software and IoT communications, thus improving mobility.
Spinning multi-beam Lidar scanners, which are relatively recent, have been introduced to meet AV industry requirements. Unlike 2D laser scanners that depend on the platform movement which covers the third dimension, the sensors have multiple beams with pairs of an emitter and a receiver for each beam. Each beam is oriented at a fixed vertical angle from the sensor origin [23]. The multi-beam mechanism spins mechanically around a spinning axis to cover 360° horizontal field of view. The frequency of the spinning axis frequency of the Velodyne Lidar sensors reaches 20 Hz, thus enabling a fast and rich 3D point cloud of the vehicle’s environment and enhancing its 3D perception. Three examples of these sensors are shown in Figure 12.
The solid-state flash Lidar is yet another Lidar technology. This technology illuminates large areas simultaneously and measures the reflected energy on a photonic phased array in an analogy of the digital camera complementary metal–oxide–semiconductor sensor. Unlike the previously-discussed sensor data measurement mechanisms, solid-state Lidar sensors do not have any moving parts and the miniaturization allows on-chip lasers [23]. Three examples of these sensors are shown in Figure 13.
It is worth noting that the current rapid increase in laser sensor technology is being driven by the AV application domain. The solid-state Lidar sensors are very promising as they are specifically designed for vehicle environment grid occupancy detection and collision avoidance. As the technology advances, however, it is anticipated that the solid-state Lidar technology will have a substantially positive effect on the 3D mapping field. Two Livox Lidar sensors are available: Mid-70 which has zero blind spot and Avia which has a detection range of up to 450 m along with the multi-scanning modes (repetitive and non-repetitive). These sensors better meet the needs for low-speed autonomous driving (Mid-70 sensor) and such applications as topographic surveying and mapping and power line inspection (Avia sensor). The sensors are shown in Figure 14.
3D maps are a key infrastructure component needed for smart cities and smart transportation applications. Mobile mapping allows the generation of 3D maps for very large areas, which are considered cost-prohibitive to be mapped using conventional mapping equipment. Active sensors are the primary RSS in a number of mobile mapping systems. A mobile mapping system (MMS) is a moving platform (typically a vehicle) where the Lidar-based system is mounted on. This setup allows capturing data of large areas quickly, which is needed if conventional terrestrial mapping is to be used. Mapping-grade MMS normally achieves sub-meter accuracy, while survey-grade MMS achieves cm-level accuracy, where cost is typically US$400 k and US$1 M, respectively.
Several factors hinder the current mobile mapping systems usage for many user segments, including major investment, operational cost, unease of deployment, and required level of expertise. The design, development, and implementation of a new Lidar-based generic 3D mapping system has been carried out at the Department of Civil Engineering, Ryerson University [24, 25]. The developed system uses the relatively low-cost recently released RSS. The optimized selection of the sensors, the smart integration of the 3D mapping system components on both the hardware and software allow a higher level of versatility, ease of deployment, and substantial cost reduction, compared to commercially systems, while maintaining a comparable accuracy metrics. The system can be used on drones, cars, or as a stationary device. The characteristics of the new system are shown in Table 1.
Feature | Value |
---|---|
System accuracy | 5 cm @ 50 m (1σ) |
Weight (battery included) Diameter | 1.5 kg 10.3 cm |
Height | 15 cm |
HFOV (VFOV) | 360° (+15° to −15°) |
Data | 300,000 pts./sec (single return) 600,000 pts./sec (dual return) |
Power consumption | 19 W (Autonomy ∼1.2 hr) |
Operating Temperature: | -10°C to +60°C |
Features of new Lidar 3D mapping system.
A sample of the 3D point cloud for a block at Ryerson University campus collected by the new system and colorized by height is shown in Figure 15. The figure clearly shows thin features such as overhead electricity wires, the traveled way, sidewalk, pedestrian crosswalk, traffic lights, and other vehicles.
The new Lidar-based system holds great potential for implementation in 3D map generation for AV. The drive behind the Lidar sensor technology advancement is to build a very low-cost, miniaturized sensor that can provide the AV with the ability of a robust 3D perception of the environment. A number of different competing factors need to be optimized, including sensor characteristics, such as range, range precision, horizontal and vertical fields of view, and size and power consumption. The main objective is to allow obstacle detection in a fast and reliable manner even at long ranges, thus ensuring safe vehicle operation.
The new Lidar sensors can benefit a multitude of application domains within the transportation sector. Those sensors are used to create the 3D maps that serve as the infrastructure needed for smart cities and smart transportation applications. Besides, Lidar sensors provide AV with its ability to perceive the 3D environment.
In addition, laser scanners can measure the amount of energy that has been reflected from the target after being illuminated with the scanner emitted pulse. The amount of reflected energy from each measured point constitutes the intensity as measured by the sensor. This measurement can prove valuable in a number of applications as asset management and lane marking extraction. The measured intensity depends on the target reflectivity, which affects the amount of energy reflected that can be detected by the sensor. As the target reflectivity decreases, the amount of reflected energy diminishes, thus weakening the signal returned to the sensor and deeming the target undetectable. The range and incidence angle to the target also affect the measured intensity.
Pavement markings have a different reflectance than the asphalt, thus allowing the automated extraction of the markings from the Lidar measured object intensities. This enables lane mapping and also serves as a tool that can be deployed for pavement marking assessment and evaluation. Street signs and furniture can be automatically extracted from the Lidar measured 3D point cloud. This is done using the fusion of the measured intensities, coupled with some machine learning algorithms that uses features geometry which is also measured by Lidar sensors.
A sample of a 3D point cloud intensity showing different features is depicted in Figure 16. The numbers in the figure refer to objects as follows: lane markings (1), vehicles (2), road surface arrows (3), zebra crossings (4), bike lane (5), a building (6), and pedestrian (7). Some highlighted features from the Lidar 3D point cloud intensity depicted in Figure 16a. As noted, the Lidar data intensity can be very useful in the automated feature extraction of street furniture, pavement marking assessment and evaluation which acts as an enabler for automated asset management. Note that a true color 3D Lidar point cloud can be produced by the fusion of the Lidar 3D point cloud and optical imagery, as shown in Figure 16b.
The fusion of Lidar 3D point cloud and optical imagery can prove very useful in such applications as digital twin and 3D visualization with and enhanced visual appeal. In addition, the different modality between Lidar data and optical imagery improves automated 3D point cloud classification as the classification process uses object geometry coupled with reflectance. The continued advances in Lidar sensor technologies, IoT, AI, and big-data algorithms and with faster wireless communications technologies that support cellular data networks 5G (even the 6G networks expected in 2030s), with data transfer rate of speeds of ∼95 Gb/s, the dream of mass deployment of an advanced driver assistance system level 5 can become a reality.
High-precision maps have been identified as one of the key technologies for autonomous driving [26]. These maps need to be purpose-built and highly accurate, with great level of details, and be updated in real-time. These maps, which are particularly made for autonomous driving, provide true-ground-absolute accuracy, normally at centimeter-level (5 cm or better), and contain details organized in multiple map layers, such as base map, geometric map, semantic map, map priors and real-time knowledge [27]. Technologies that can be leveraged for creating such maps include aerial imagery, aerial Lidar data, mobile Lidar mapping, and Unmanned Aerial Vehicles equipped with lightweight sensors. On the ground, HP maps are mainly created using vehicles equipped with high-tech instruments [26]. Efforts on HP mapping in 3D include Civil Maps, HERE, DeepMap, TomTom HP Maps, Mobileye, Uber Localization and Mapping, and Mapper, while most of them follow crowdsourcing models.
In addition to supporting localization and navigation, HP 3D maps can be leveraged for lane network construction, dynamic object detection, prediction of motion of vulnerable road users, and visibility analysis for the correlation between environmental visibility and navigational comfort under autonomous driving [28, 29]. The so-called self-healing mapping systems allows a detailed inventory of road features and objects, part of road infrastructure, on the side of the road for real-time determination of infrastructure visibility.
To provide dynamic routing for any situation, Geographic Information System (GIS) requires updated road network data, real-time traffic information, vehicle current location, and destination. Real-time vehicle location is obtained using Radar and Camera sensors using localization techniques such as simultaneous localization and mapping (SLAM) that can localize vehicle with high precision and map its exact location with respect to the surrounding environment. Localization can not only determine vehicle location, but also map landmarks to update HP maps. Depending on the use of sensors, the algorithm can be Lidar-based, Lidar and camera integrated, or camera-based.
Building and maintaining detailed HP maps in advance presents a very appealing solution for autonomous driving. However, exiting technologies may at the best provide near real-time mapping, and may have missed critical information, such as road markings and dynamic changes of road infrastructure. Real-time SLAM provides better solution to dynamic updating of HP maps for locating vehicles and mapping the surrounding environment.
The technique for estimating visibility of target points described in Section 2.2 can be extended to detect sight obstacles that restrict infrastructure visibility (e.g. traffic signs). Specifically, as shown in Figure 7a, the visibility of the target points are determined by comparing their distances to the sight point with the minimum distance (i.e.
The sight obstacles can be detected in a similar way. The respective depth maps of the obstacle points and target infrastructure points are first generated following the steps presented in Section 2.2. Then, at the stage of comparing the depth maps, another parameter is calculated, as shown in Figure 7a. Let
Guaranteeing adequate traffic visibility is crucial to the safe operation of AV. If the sight lines to the conflicting objects (e.g. vehicles, pedestrians, and cyclists) are obstructed by some obstacles (Figure 17a), the AV may not identify the objects at a safe distance, which may lead to a collision. It is quite challenging for AV vehicles to predict where pedestrians or cyclists may be present in a complex road scene in real-time [30]. In this case, it is meaningful to identify the positions where road users may exist in real-world using dense MLS points and investigate the visibility of these locations to the AV in advance (see Figure 17a and b). Then the georeferenced visibility information can be incorporated into HP maps, which may help AV make some proactive measures to reduce the collision risk at locations with unsatisfactory traffic visibility.
As shown in Figure 17c, the general process of traffic visibility analysis comprises two main components: (1) critical positions and (2) driving lines. The critical positions correspond to the target points in the VE process (see Figure 4). The driving lines in HP maps which aid in navigating AV can help derive sight points in this case. Traffic visibility estimation in autonomous driving involves estimating the visibility of some critical positions along the pre-defined driving lines to the AV and generating the visibility map.
In this example, the planar ground points are identified as the locations where vehicles, pedestrians, or cyclists may exist. A common workflow shown in Figure 8a is used to extract the critical positions. More specifically, MLS data are first partitioned into a number of pillars with the linear-index based segmentation technique illustrated in Figure 11. Next, a pillar-wise filtering is performed that the points with
Extraction of critical positions: (a) general process of identifying planar ground points and (b) critical positions and driving lines.
Because we mainly focus on the locations where the road users may exist and may conflict with the AV, a distance-based segmentation method is applied to remove some isolated planar point clusters. The point cluster with the largest size is determined as the ‘critical positions’. MLS data of a 500 m long urban road section is used to illustrate the process of VE. The extracted critical positions using the procedure shown in Figure 8a are marked in red in Figure 8b. The critical positions covers pavement surface, sidewalks, and some planar surfaces connected to the pavement. It is also recommended to use the semantic segmentation techniques powered by deep learning to identify the critical positions more accurately.
The driving lines of AV are also plotted in Figure 8b. Each driving line is composed of many consecutive nodes. At each node, suppose that the sight point overlap with the node on the horizontal plane. However, the elevation of sight point
VE results at a single node.
The VE procedure is executed along the driving lines node by node (see Figure 10a) to gain a better understanding of traffic visibility for AV. In this phase, the variables involved are hs = 1.6 m, dview = 100 m, φview = −60 to 60
Visualization of VE results: (a) VE process, (b) cumulative times of invisibility, and (c) visibility ratio.
Linear index-based segmentation: (a) process and (b) time performance.
Examples of spinning multi-beam laser sensors: (a) Velodyne VLP-32c, (b) Hesai Pandar64, and (c) Quanergy M8. Source: (a) www.velodyne.com (b) www.hesaitech.com, and (c) www.quanergy.com.
Examples of solid state Lidar: (a) Velodyne Velarray: (b) Quanergy S3, and (c) Leddartech M16-LSR. Source: (a) www.velodyne.com, (b) www.quanergy.com, and (c) www.leddartech.com.
Examples of the Livox Lidar sensors: (a) Livox Mid-70 and (b) Livox Avia. Source: www.livoxtech.com.
3D point cloud of part of Ryerson University campus, showing street furniture (color-coded by height).
Lidar 3D point cloud: (a) intensity and (b) true color.
Traffic visibility estimation for AV: (a) reasons for estimating traffic visibility, (b) critical positions, and (c) general workflow of traffic visibility estimation.
The VE results are visualized in Figure 10. In this example, two types of quantitative information are derived based on VE results at each node. Figure 10b maps the cumulative times of invisibility
Figure 10c shows the variation of the visibility ratio
This chapter has addressed so far the visibility element of autonomous vehicles, which is the main focus of the chapter. It is useful, however, to highlight the decision element (see Figure 1), which involves path planning. This element is considered to be the main challenge of autonomous vehicles. Path planning enables the autonomous vehicle to find the safest, most convenient, and most economic route between two points. One of the main modeling approaches to define such a route is Model Predictive Control (MPC). There is a variety of MPC, but basically the model solves a finite-time constrained optimal control problem in a receding horizon using nonlinear optimization. An application of path planning and control for overtaking on two-lane highways using nonlinear MPC can be found in [32].
There are two approaches for path planning (hierarchical and parallel) [33]. In the hierarchical approach the autonomous vehicle completes long-term mission and reduce the workload of motion planning. The input higher-level mission is decomposed into sub-missions and then passed to the next level down. The hierarchical model helps to resolve many complicated problems, yet it might slow down the work of a vehicle’s feedback control and complicate the performance of sophisticated maneuvers. In the parallel approach, the tasks are more independent and can proceed simultaneously. In this approach, each controller has dedicated sensors and actuation mechanisms. The advantages of this approach: (1) the controllers are running at high frequency, which makes them safe and stable, (2) a high level of smoothness and performance is achieved by the controllers, and (3) the approach is relatively inexpensive and does not require using complicated motion planning devices. However, for some purposes, the hierarchical approach is more efficient. Figure 18 illustrates the hierarchical approach, based on [33].
Hierarchical approach for path planning.
Extensive research has been conducted for path planning for automated parking, which is now a reality in many cities [34, 35, 36, 37, 38]. Automated parking aims to eliminate the influence of human factors, improve the quality and accuracy of control, and reduce the maneuver time by optimizing vehicle path in restricted parking zones. The vehicle to anything technology (V2X) allows communication between AV and a building through data exchange to find an unoccupied space and generate the route to a destination. For large-sized trucks, the tasks involve predicting stable and safe passing on road curves and forecasting precise control for docking. Numerous approaches that consider different control strategies, sensory means, and prediction algorithms (e.g. geometric, fuzzy, and neural) for predicting vehicle parking path have been developed.
Recently, the bicycle kinematic models for vehicle motion have been used for path planning of automated parking [39]. Three basic types of vehicles were considered: passenger car, long wheelbase truck, and articulated vehicles with and without steered semitrailer axes. The authors presented a system of differential equations in matrix form and expressions for linearizing the nonlinear motion equations that increased the speed of finding the optimal solution. An original algorithm that considered numerous constraints was developed for determining vehicle permissible positions within the closed boundaries of the parking area using nonlinear MPC that finds the best trajectories.
Figure 19 shows the kinematics of the curvilinear motion and the simulation results that validated the proposed model. Note that in this study, kinematic vehicle models were used instead of dynamic models. Kinematic models assume that no slip occurs between the wheels and the road. This assumption is reasonable for vehicles moving at low speeds, which is the case for parking. However, dynamic models should be used for path planning of autonomous vehicles on highways since they are more accurate.
Modeling and simulation of autonomous docking of tractor-semitrailer vehicle with semitrailer’s steered axles: (a) kinematics of curvilinear motion and (b) simulation results [39].
Autonomous vehicles and connected automated vehicles (CAV) with advanced embedded technology can deliver safe and effective traffic movements [40]. However, there will be a transition period when AV and human-driven vehicles would share the common road space. Therefore, it would be extremely critical to organize the interactions which will likely be generated from that vehicle mix. Due to the anticipated asymmetric response of these two modes of vehicles, many possible combinations of these vehicles are possible. These combinations may give rise to interactions due to longitudinal and transverse movements.
To ensure safe interactions, the intelligent vehicles may include adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) systems. These systems mainly assist the acceleration control for the longitudinal movements. The systems control the acceleration based on the distance gap and the speed difference between the current vehicle and the vehicle ahead (leader), where the vehicle accelerates and decelerates based on the speed changes of the leader. For the ACC system, the distance and speed are obtained using radar, Lidar, or video cameras. For the CACC system, V2V communications are used to share the acceleration, deceleration, breaking capability, and vehicle positions [41]. This communication significantly shortens the time headway (0.5 s) of the CACC vehicle compared to the ACC vehicle (1.4 s).
To obtain deeper insights into car-following behavior, micro simulation studies were conducted to estimate the impacts of AV and CAV using a variety of assumptions [41]. Car-following models involving intelligent vehicles were developed to evaluate their traffic impacts on such aspects as capacity and level of service, traffic stability, travel time, and vehicle speed. Several studies estimated the energy, environmental, and safety impacts using surrogate safety measures, such as travel speed, time-to-collision, and post-encroachment-time.
New car-following models for AV and CAV were developed by modifying available traditional car-following models to mimic the intelligent vehicle characteristics. The Intelligent Driver Model (IDM) [42], a time-continuous car-following model for the simulation of freeway and urban traffic, is the most commonly used model for simulations of intelligent vehicles. The basic function of the model is given by
where s = current distance gap to the leader (m), so = minimum distance gap (m), v = current speed (m/s), vo = desired safety speed (m/s), Δv = speed difference between the current vehicle and the leader (m/s), δ = parameter that determines the magnitude of acceleration decrease (usually set equal to 4), T = desired safety gap (s), a = maximum acceletation (m/s2), and b = comfortable deceleration rate (m/s2). Note that s*(v, Δv) is the desired distance gap (m).
Another car-following model that is commonly used models for conducting cooperative intelligent-vehicle simulations is the MICroscopic Model for Simulation of Intelligent Cruise Control (MIXIC) [43]. Both IDM and MIXIC have been used as the benchmark models for combined AV and human-driven vehicles for various market penetration rates ranging from 0 to 25% [44, 45]. However, detailed calibration procedures of model parameters used in these models are not available due to the limited or unavailability of empirical data.
On these lines, substantiating vehicle’s longitudinal motion in mixed traffic conditions is even more critical using available car-following model parameters. In the present context, the car-following models used in simulation packages need to be suitably modified. Further, most of these models are straightforward in their approach with a limited set of parameters. The simplification of available model parameters might not affect the simulation results to the extent the intelligent vehicles may impact the traffic streams in real-field experiments. For example, most car-following models tend to have fixed parameters and will average out driving characteristics. If such models are used for AV, they can reflect neither the driving style of the vehicles’ actual drivers nor the contexts in which they drive.
In this direction, the intelligent car-following behavior models, which can concurrently consider all necessary parameters and adapt to specific field actions must successfully integrate possible mixed traffic scenarios. Hence, there is a strong need to modify the existing conventional models to handle the higher order of stochasticity due to mixed driving conditions. Alternatively, data-driven methodologies can be used to provide high-quality trajectory data for varying scenarios of automation. These data may be generated from experiments by creating test beds or even using driving simulators with human-machine interaction. For this purpose, the trajectory data available from simulation models may also be useful. Considering the recent highlights of AI, even intelligent car-following models can benefit from data learning methodologies. Clearly, the impact analysis of intelligent vehicles should consider the many uncertainties due to mixed traffic conditions. Although a few new models have been developed or modified to incorporate car-following and lane-changing attributes of intelligent vehicles, empirical data are warranted for model calibration.
This chapter has presented an overview of the visibility-based technologies and methodologies for autonomous driving. Based on this overview, the following comments are offered:
The Lidar height is a key parameter that affects the visibility of the road ahead and in turn sight distance that influences the design of horizontal and vertical alignment as well as intersections. To ensure safety of AV when operating with human-driven vehicles on existing highways, the Lidar height should generally be larger than the current design heights for passenger cars and less than that for trucks.
The new Lidar system developed at Ryerson University holds great potential for generating 3D maps for AV. The system is cheap, allows AV a robust 3D perception of the environment, and allows obstacle detection in a fast and reliable manner even at long ranges.
The infrastructure and traffic visibilities can be estimated for autonomous vehicles based on a combination of MLS data and the driving lines of HP maps. The generated visibility map can be incorporated into the HP maps and help the autonomous vehicle develop some proactive speed control strategies at locations where the visibility is unsatisfactory.
The uncertainty of the input variables can substantially affect the reliability of the design elements. Therefore, reliability analysis should be incorporated in all the tasks related to SD determination to ensure safe operation of the autonomous vehicles. Extensive uncertainty research has been conducted on the required SD and similar research is lacking for the available SD determined using the Lidar.
Geographic information systems provide support for route planning and real-time and dynamic routing/navigation of AV using GNSS, localization techniques, and HP maps. The GIS navigation helps to guide the autonomous vehicle along the best route to its destination safely.
The intelligent car-following strategies are expected to show good performance in simulation environments. However, in the calibration using real-field conditions/experiments, the input to simulation models would not be beneficial as it is limited to the available parameters that correspond to specific conditions. The parameters should be based on the surrounding environmental factors for all possible combinations of autonomous and human-driven vehicles. This is a mammoth challenge due to the limited empirical data.
Another challenge of autonomous driving is related to the complexity of the software algorithms needed to process the large amount of information coming into the AV (estimated at 1 billion lines of code) which is then used to make decisions about the proper action. The software complexity is compounded by the fact that AV will operate on roads full of unpredictable drivers of human-driven vehicles.
This chapter is financially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).
ACC | adaptive cruise control |
AI | artificial intelligence |
ALS | airborne laser scanning |
AV | autonomous vehicles |
CACC | cooperative adaptive cruise control |
CAV | connected automated vehicles |
DSD | decision sight distance |
GIS | geographical information system |
GNSS | global navigation satellite system |
HP | high-precision |
IDM | intelligent driver model |
IoT | internet of things |
ISD | intersection sight distance |
Lidar | light detection and ranging |
LOS | line of sight |
MLS | mobile laser scanning |
MSS | mobile mapping system |
PSD | passing sight distance |
SD | sight distance |
SLAM | simultaneous localization and mapping |
SSD | stopping sight distance |
RSS | remote sensing sensor |
VE | visibility estimation |
V2I | vehicle-to-infrastructure |
V2V | vehicle-to-vehicle |
1D | one dimensional |
3D | three dimensional |
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\\n\\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
\\n\\nThe Author agrees to indemnify IntechOpen harmless against all liabilities, costs, expenses, damages and losses, as well as all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of, or in connection with, any breach of the agreed confirmations and warranties. This indemnity shall not apply in a situation in which a claim results from IntechOpen's negligence or willful misconduct.
\\n\\nNothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\\n\\nTERMINATION
\\n\\nIntechOpen has the right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Author and/or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Author and/or any Co-Author (being a private individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Author and/or any Co-Author (as a corporate entity) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for, or enters into, any compromise or arrangement with any of its creditors.
\\n\\nIn the event of termination, IntechOpen will notify the Author of the decision in writing.
\\n\\nIntechOpen’s DUTIES AND RIGHTS
\\n\\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen, at its discretion, agrees to publish the Work attributing it to the Author and Co-Authors.
\\n\\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen agrees to provide publishing services which include: managing editing (editorial and publishing process coordination, Author assistance); publishing software technology; language copyediting; typesetting; online publishing; hosting and web management; and abstracting and indexing services.
\\n\\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\\n\\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\\n\\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
\\n\\nMISCELLANEOUS
\\n\\nFurther Assurance: The Author shall ensure that any relevant third party, including any Co-Author, shall execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\\n\\nThird Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\\n\\nEntire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by, or on behalf of, the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (known as the "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of any fraudulent pre-contract misrepresentation or concealment.
\\n\\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\\n\\nVariation: No variation of this Publication Agreement shall have effect unless it is in writing and signed by the parties, or their duly authorized representatives.
\\n\\nSeverance: If any provision, or part-provision, of this Publication Agreement is, or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted. Any modification to, or deletion of, a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\\n\\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\\n\\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
\\n\\nPolicy last updated: 2018-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'When submitting a manuscript, the Author is required to accept the Terms and Conditions set out in our Publication Agreement – Monographs/Compacts as follows:
\n\nCORRESPONDING AUTHOR'S GRANT OF RIGHTS
\n\nSubject to the following Article, the Author grants to IntechOpen, during the full term of copyright, and any extensions or renewals of that term, the following:
\n\nThe foregoing licenses shall survive the expiry or termination of this Publication Agreement for any reason.
\n\nThe Author, on his or her own behalf and on behalf of any of the Co-Authors, reserves the following rights in the Work but agrees not to exercise them in such a way as to adversely affect IntechOpen's ability to utilize the full benefit of this Publication Agreement: (i) reprographic rights worldwide, other than those which subsist in the typographical arrangement of the Work as published by IntechOpen; and (ii) public lending rights arising under the Public Lending Right Act 1979, as amended from time to time, and any similar rights arising in any part of the world.
\n\nThe Author, and any Co-Author, confirms that they are, and will remain, a member of any applicable licensing and collecting society and any successor to that body responsible for administering royalties for the reprographic reproduction of copyright works.
\n\nSubject to the license granted above, copyright in the Work and all versions of it created during IntechOpen's editing process, including all published versions, is retained by the Author and any Co-Authors.
\n\nSubject to the license granted above, the Author and Co-Authors retain patent, trademark and other intellectual property rights to the Work.
\n\nAll rights granted to IntechOpen in this Article are assignable, sublicensable or otherwise transferrable to third parties without the specific approval of the Author or Co-Authors.
\n\nThe Author, on his/her own behalf and on behalf of the Co-Authors, will not assert any rights under the Copyright, Designs and Patents Act 1988 to object to derogatory treatment of the Work as a consequence of IntechOpen's changes to the Work arising from the translation of it, corrections and edits for house style, removal of problematic material and other reasonable edits as determined by IntechOpen.
\n\nAUTHOR'S DUTIES
\n\nWhen distributing or re-publishing the Work, the Author agrees to credit the Monograph/Compacts as the source of first publication, as well as IntechOpen. The Author guarantees that Co-Authors will also credit the Monograph/Compacts as the source of first publication, as well as IntechOpen, when they are distributing or re-publishing the Work.
\n\nThe Author agrees to:
\n\nThe Author will be held responsible for the payment of the agreed Open Access Publishing Fee before the completion of the project (Monograph/Compacts publication).
\n\nAll payments shall be due 30 days from the date of issue of the invoice. The Author or whoever is paying on behalf of the Author and Co-Authors will bear all banking and similar charges incurred.
\n\nThe Author shall obtain in writing all consents necessary for the reproduction of any material in which a third-party right exists, including quotations, photographs and illustrations, in all editions of the Work worldwide for the full term of the above licenses, and shall provide to IntechOpen, at its request, the original copies of such consents for inspection or the photocopies of such consents.
\n\nThe Author shall obtain written informed consent for publication from those who might recognize themselves or be identified by others, for example from case reports or photographs.
\n\nThe Author shall respect confidentiality during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Author and Co-Authors are confidential and are intended only for the recipients. The contents of any communication may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\n\nAUTHOR'S WARRANTY
\n\nThe Author and Co-Authors confirm and warrant that the Work does not and will not breach any applicable law or the rights of any third party and, specifically, that the Work contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy.
\n\nThe Author and Co-Authors confirm that: (i) the Work is their original work and is not copied wholly or substantially from any other work or material or any other source; (ii) the Work has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) Authors and any applicable Co-Authors are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) Authors and any applicable Co-Authors have not assigned, and will not during the term of this Publication Agreement purport to assign, any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\n\nThe Author and Co-Authors also confirm and warrant that: (i) he/she has the power to enter into this Publication Agreement on his or her own behalf and on behalf of each Co-Author; and (ii) has the necessary rights and/or title in and to the Work to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licences in this Publication Agreement. If the Work was prepared jointly by the Author and Co-Authors, the Author confirms that: (i) all Co-Authors agree to the submission, license and publication of the Work on the terms of this Publication Agreement; and (ii) the Author has the authority to enter into this biding Publication Agreement on behalf of each Co-Author. The Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each Co-Author.
\n\nThe Author agrees to indemnify IntechOpen harmless against all liabilities, costs, expenses, damages and losses, as well as all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of, or in connection with, any breach of the agreed confirmations and warranties. This indemnity shall not apply in a situation in which a claim results from IntechOpen's negligence or willful misconduct.
\n\nNothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\nTERMINATION
\n\nIntechOpen has the right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Author and/or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Author and/or any Co-Author (being a private individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Author and/or any Co-Author (as a corporate entity) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for, or enters into, any compromise or arrangement with any of its creditors.
\n\nIn the event of termination, IntechOpen will notify the Author of the decision in writing.
\n\nIntechOpen’s DUTIES AND RIGHTS
\n\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen, at its discretion, agrees to publish the Work attributing it to the Author and Co-Authors.
\n\nUnless prevented from doing so by events beyond its reasonable control, IntechOpen agrees to provide publishing services which include: managing editing (editorial and publishing process coordination, Author assistance); publishing software technology; language copyediting; typesetting; online publishing; hosting and web management; and abstracting and indexing services.
\n\nIntechOpen agrees to offer free online access to readers and use reasonable efforts to promote the Publication to relevant audiences.
\n\nIntechOpen is granted the authority to enforce the rights from this Publication Agreement on behalf of the Author and Co-Authors against third parties, for example in cases of plagiarism or copyright infringements. In respect of any such infringement or suspected infringement of the copyright in the Work, IntechOpen shall have absolute discretion in addressing any such infringement that is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\nIntechOpen has the right to include/use the Author and Co-Authors names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Work and has the right to contact the Author and Co-Authors until the Work is publicly available on any platform owned and/or operated by IntechOpen.
\n\nMISCELLANEOUS
\n\nFurther Assurance: The Author shall ensure that any relevant third party, including any Co-Author, shall execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\nThird Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\nEntire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by, or on behalf of, the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (known as the "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of any fraudulent pre-contract misrepresentation or concealment.
\n\nWaiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\nVariation: No variation of this Publication Agreement shall have effect unless it is in writing and signed by the parties, or their duly authorized representatives.
\n\nSeverance: If any provision, or part-provision, of this Publication Agreement is, or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted. Any modification to, or deletion of, a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\n\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
\n\nPolicy last updated: 2018-09-11
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