Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Localization and Mapping for Self-Driving Vehicles:A Survey

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Vision based environment perception system for next generation off-road ADAS : innovation report

Advanced Driver Assistance Systems (ADAS) aids the driver by providing information or automating the driving related tasks to improve driver comfort, reduce workload and improve safety. The vehicle senses its external environment using sensors, building a representation of the world used by the control systems. In on-road applications, the perception focuses on establishing the location of other road participants such as vehicles and pedestrians and identifying the road trajectory. Perception in the off-road environment is more complex, as the structure found in urban environments is absent. Off-road perception deals with the estimation of surface topography and surface type, which are the factors that will affect vehicle behaviour in unstructured environments. Off-road perception has seldom been explored in automotive context. For autonomous off-road driving, the perception solutions are primarily related to robotics and not directly applicable in the ADAS domain due to the different goals of unmanned autonomous systems, their complexity and the cost of employed sensors. Such applications consider only the impact of the terrain on the vehicle safety and progress but do not account for the driver comfort and assistance. This work addresses the problem of processing vision sensor data to extract the required information about the terrain. The main focus of this work is on the perception task with the constraints of automotive sensors and the requirements of the ADAS systems. By providing a semantic representation of the off-road environment including terrain attributes such as terrain type, description of the terrain topography and surface roughness, the perception system can cater for the requirements of the next generation of off-road ADAS proposed by Land Rover. Firstly, a novel and computationally efficient terrain recognition method was developed. The method facilitates recognition of low friction grass surfaces in real-time with high accuracy, by applying machine learning Support Vector Machine with illumination invariant normalised RGB colour descriptors. The proposed method was analysed and its performance was evaluated experimentally in off-road environments. Terrain recognition performance was evaluated on a variety of different surface types including grass, gravel and tarmac, showing high grass detection performance with accuracy of 97%. Secondly, a terrain geometry identification method was proposed which facilitates semantic representation of the terrain in terms of macro terrain features such as slopes, crest and ditches. The terrain geometry identification method processes 3D information reconstructed from stereo imagery and constructs a compact grid representation of the surface topography. This representation is further processed to extract object representation of slopes, ditches and crests. Thirdly, a novel method for surface roughness identification was proposed. The surface roughness descriptor is then further used to recommend a vehicle velocity, which will maintain passenger comfort. Surface roughness is described by the Power Spectral Density of the surface profile which correlates with the acceleration experienced by the vehicle. The surface roughness descriptor is then mapped onto vehicle speed recommendation so that the speed of the vehicle can be adapted in anticipation of the surface roughness. Terrain geometry and surface roughness identification performance were evaluated on a range of off-road courses with varying topology showing the capability of the system to correctly identify terrain features up to 20 m ahead of the vehicle and analyse surface roughness up to 15 m ahead of the vehicle. The speed was recommended correctly within +/- 5 kph. Further, the impact of the perception system on the speed adaptation was evaluated, showing the improvements in speed adaptation allowing for greater passenger comfort. The developed perception components facilitated the development of new off-road ADAS systems and were successfully applied in prototype vehicles. The proposed off-road ADAS are planned to be introduced in future generations of Land Rover products. The benefits of this research also included new Intellectual Property generated for Jaguar Land Rover. In the wider context, the enhanced off-road perception capability may facilitate further development of off-road automated driving and off-road autonomy within the constraints of the automotive platfor

Improvement of Low Traffic Volume Gravel Roads in Nebraska

In the state of Nebraska, over one-third of roadways are unpaved, and consequently require a significant amount of financial and operational resources to maintain their operation. Undesired behavior of surface gravel aggregates and the road surfaces can include rutting, corrugation, and ponding that may lead to reduced driving safety, speed or network efficiency, and fuel economy. This study evaluates the parameters that characterize the performance and condition of gravel roads overtime period related to various aggregate mix designs. The parameters, including width, slope, and crown profiles, are examples of performance criteria. As remote sensing technologies have advanced in the recent decade, various techniques have been introduced to collect high quality, accurate, and dense data efficiently that can be used for roadway performance assessments. Within this study, two remote sensing platforms, including an unpiloted aerial system (UAS) and ground-based lidar scanner, were used to collect point cloud data of selected roadway sites with various mix design constituents and further processed for digital assessments. Within the assessment process, statistical parameters such as standard deviation, mean value, and coefficient of variance are calculated for the extracted crown profiles. In addition, the study demonstrated that the point clouds obtained from both lidar scanners and UAS derived SfM can be used to characterize the roadway geometry accurately and extract critical information accurately

Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications

This study presents a comprehensive investigation of methods and technologies for developing a computer vision-based framework for Structural Health Monitoring (SHM) and Structural Identification (St-Id) for civil infrastructure systems, with particular emphasis on various types of bridges. SHM is implemented on various structures over the last two decades, yet, there are some issues such as considerable cost, field implementation time and excessive labor needs for the instrumentation of sensors, cable wiring work and possible interruptions during implementation. These issues make it only viable when major investments for SHM are warranted for decision making. For other cases, there needs to be a practical and effective solution, which computer-vision based framework can be a viable alternative. Computer vision based SHM has been explored over the last decade. Unlike most of the vision-based structural identification studies and practices, which focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation, the proposed framework combines the vision-based structural input and the structural output from non-contact sensors to overcome the limitations given above. First, this study develops a series of computer vision-based displacement measurement methods for structural response (structural output) monitoring which can be applied to different infrastructures such as grandstands, stadiums, towers, footbridges, small/medium span concrete bridges, railway bridges, and long span bridges, and under different loading cases such as human crowd, pedestrians, wind, vehicle, etc. Structural behavior, modal properties, load carrying capacities, structural serviceability and performance are investigated using vision-based methods and validated by comparing with conventional SHM approaches. In this study, some of the most famous landmark structures such as long span bridges are utilized as case studies. This study also investigated the serviceability status of structures by using computer vision-based methods. Subsequently, issues and considerations for computer vision-based measurement in field application are discussed and recommendations are provided for better results. This study also proposes a robust vision-based method for displacement measurement using spatio-temporal context learning and Taylor approximation to overcome the difficulties of vision-based monitoring under adverse environmental factors such as fog and illumination change. In addition, it is shown that the external load distribution on structures (structural input) can be estimated by using visual tracking, and afterward load rating of a bridge can be determined by using the load distribution factors extracted from computer vision-based methods. By combining the structural input and output results, the unit influence line (UIL) of structures are extracted during daily traffic just using cameras from which the external loads can be estimated by using just cameras and extracted UIL. Finally, the condition assessment at global structural level can be achieved using the structural input and output, both obtained from computer vision approaches, would give a normalized response irrespective of the type and/or load configurations of the vehicles or human loads