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SAE EDGE Research Reports provide state-of-the-art and state-of-industry examinations of the most significant topics in mobility engineering. SAE EDGE contributors are experts from research, academia, and industry who have come together to explore and define the most critical advancements, challenges, and future direction in areas such as vehicle automation, unmanned aircraft, IoT and connectivity, cybersecurity, advanced propulsion, and advanced manufacturing.
Thousands die or are injured each year in automobile crashes. Reducing the number of these tragedies requires reframing our approach to vehicle- and human-based transportation mobility and depends on whether the mobility industry and individual human drivers take a more aggressive approach to saving lives and preventing injuries. Bringing automated driving systems technologies into the advanced driver assist systems (ADAS) and connected vehicle space will help humans drive more safely and better prepare us for automated vehicles (AVs). Reducing Human Driver Error and Setting Realistic Expectations with Advanced Driver Assistance Systems discusses the recent Partnership for Analytics Research in Traffic Safety report which shows that ADAS can indeed work. The path forward requires combining ADAS and ADS implementation with infrastructure engineering, law enforcement, education, emergency response, and public policy, with the goal of reaching zero deaths and serious injuries. It also requires fully embracing the US Department of Transportation Federal Highway Administration’s Safe System approach, backed by the addition of public policies that incorporate and expand ADAS’s role in achieving that safe system. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2023016
This book describes different methods that are relevant to the development and testing of control algorithms for advanced driver assistance systems (ADAS) and automated driving functions (ADF). These control algorithms need to respond safely, reliably and optimally in varying operating conditions. Also, vehicles have to comply with safety and emission legislation. The text describes how such control algorithms can be developed, tested and verified for use in real-world driving situations. Owing to the complex interaction of vehicles with the environment and different traffic participants, an almost infinite number of possible scenarios and situations that need to be considered may exist. The book explains new methods to address this complexity, with reference to human interaction modelling, various theoretical approaches to the definition of real-world scenarios, and with practically-oriented examples and contributions, to ensure efficient development and testing of ADAS and ADF. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions is a collection of articles by international experts in the field representing theoretical and application-based points of view. As such, the methods and examples demonstrated in the book will be a valuable source of information for academic and industrial researchers, as well as for automotive companies and suppliers.
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;DRIVER MODELLING;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation systems
Autonomous Driving and Advanced Driver-Assistance Systems (ADAS): Applications, Development, Legal Issues, and Testing outlines the latest research related to autonomous cars and advanced driver-assistance systems, including the development, testing, and verification for real-time situations of sensor fusion, sensor placement, control algorithms, and computer vision. Features: Co-edited by an experienced roboticist and author and an experienced academic Addresses the legal aspect of autonomous driving and ADAS Presents the application of ADAS in autonomous vehicle parking systems With an infinite number of real-time possibilities that need to be addressed, the methods and the examples included in this book are a valuable source of information for academic and industrial researchers, automotive companies, and suppliers.
In recent years, vehicle electrification has risen due to the increasingly stringent polices put in place to reduce greenhouse gas emissions in the transportation industry. At the same time, research and development efforts in Connected and Autonomous Vehicles (CAVs) has grown substantially due to the advancement of new technologies that has encouraged the deployment of semi-autonomous vehicles. Vehicles with partial or conditional automation require a collaboration between the vehicle control system and the human driver for safe execution of maneuvers. As a result, humans play a critical role in the development and deployment of Advanced Driver Assistance Systems (ADAS), warranting the need to understand the human-machine interaction issues related to these systems, and to analyze their effects on vehicle performance and energy consumption. This work investigates the effects of the interactions between a human driver and a vehicle equipped with ADAS, focusing on the case of a human in the loop with a vehicle speed advisory system. To this end, a simulation study is conducted to evaluate the importance of modeling the driver behavior when optimizing the vehicle velocity for Eco-Driving. An optimization study is conducted via dynamic programming, incorporating driver behavior and its response to a velocity advisory. Next, an investigation is conducted to evaluate the accuracy of different mathematical models predicting driver behavior in the context of ADAS. To this end, an experimental study was conducted on a driving simulator where human drivers were compared with respect to their ability to follow a velocity advisor. Data collected from the driver simulator were used to calibrate a deterministic and a stochastic driver model, and compare their ability to replicate realistic velocity profiles and driver error.
This text presents part of the IMechE seminar proceedings that cover the technological and engineering issues of Advanced Driver Assistance Systems as well as examining questions regarding driver and market acceptability, safety risks and emerging standardization.
The comprehension of a traffic situation plays a major role in driving a vehicle. Interpretable information forms a basis for future projection, decision making and action performing, such as navigating, maneuvering and driving control. Michael Huelsen provides an ontology-based generic traffic situation description capable of supplying various advanced driver assistance systems with relevant information about the current traffic situation of a vehicle and its environment. These systems are enabled to perform reasonable actions and approach visionary goals such as injury and accident free driving, substantial assistance in arbitrary situations up to even autonomous driving.
Advanced Driver-Assistance Systems (ADAS) provide the opportunity to increase road safety and driving comfort. Reviewing existing empirical work on comparable innovations, Patrick Planing derives potential acceptance constructs, which together with the results of thirty-two semi-structured interviews, have constituted the basis for a survey instrument that was consequently administered to a sample of over 400 participants from the target population. The resulting regression model shows that perceived safety and comfort benefits are most decisive for the acceptance of ADAS, while desire to exert control was found to most strongly support resistance to this technology.
This book provides a comprehensive reference for both academia and industry on the fundamentals, technology details, and applications of Advanced Driver-Assistance Systems (ADAS) and autonomous driving, an emerging and rapidly growing area. The book written by experts covers the most recent research results and industry progress in the following areas: ADAS system design and test methodologies, advanced materials, modern automotive technologies, artificial intelligence, reliability concerns, and failure analysis in ADAS. Numerous images, tables, and didactic schematics are included throughout. This essential book equips readers with an in-depth understanding of all aspects of ADAS, providing insights into key areas for future research and development. • Provides comprehensive coverage of the state-of-the-art in ADAS • Covers advanced materials, deep learning, quality and reliability concerns, and fault isolation and failure analysis • Discusses ADAS system design and test methodologies, novel automotive technologies • Features contributions from both academic and industry authors, for a complete view of this important technology