46,816 research outputs found

    Area-wide real-world test scenarios of poor visibility for safe development of automated vehicles

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    Introduction Automated vehicles in everyday real-world traffic are predicted to be developed soon (Gasser et al., Rechtsfolgen zunehmender Fahrzeugautomatisierung, Wirtschaftsverlag NW, Berichte der Bundesanstalt für Straßenwesen F83, 2012). New technologies such as advanced object detection and artificial intelligence (AI) that use machine or deep-learning algorithms will support meeting all the maneuvering challenges involved in different degrees of automation (Society of Automotive Engineers - SAE international, Levels of driving automation for on road vehicles, Warrendale, PA., 2014; National Highway Traffic Safety Administration – NHTSA, Preliminary statement of policy concerning automated vehicles, Washington, DC, 2018). For automated series production, these vehicles of course must be safe in real-world traffic under all weather conditions. Therefore, system validation, ethical aspects and testing of automated vehicle functions are fundamental basics for successfully developing, market launching, ethical and social acceptance. Method In order to test and validate critical poor visibility detection challenges of automated vehicles with reasonable expenditure, a first area-wide analysis has been conducted. Because poor visibility restricts human perception similar corresponding to machine perception it was based on a text analysis of 1.28 million area-wide police accident reports – followed by an in-depth case-by-case analysis of 374 identified cases concerning bad weather conditions (see chap. 1.3). For this purpose the first time ever a nationwide analysis included all police reports in the whole area within the state of Saxony from the year 2004 until 2014. Results Within this large database, 374 accidents were found due to perception limitations – caused by “rain”, “fog”, “snow”, “glare”/“blinding” and “visual obstruction” – for the detailed case-by-case investigation. All those challenging traffic scenarios are relevant for automated driving. They will form a key aspect for safe development, validation and testing of machine perception within automated driving functions. Conclusions This first area-wide analysis does not only rely on samples as in previous in-depth analyses. It provides relevant real-world traffic scenarios for testing of automated vehicles. For the first time this analysis is carried out knowing the place, time and context of each accident over the total investigated area of an entire federal state. Thus, the accidents that have been analyzed include all kinds of representative situations that can occur on motorways, highways, main roads, side streets or urban traffic. The scenarios can be extrapolated to include similar road networks worldwide. These results additionally will be taken into account for developing standards regarding early simulations as well as for the subsequent real-life testing. In the future, vehicle operation data and traffic simulations could be included as well. Based on these relevant real-world accidents culled from the federal accident database for Saxony, the authors recommend further development of internationally valid guidelines based on ethical, legal requirements and social acceptance. Document type: Articl

    Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges

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    The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally automated (e.g., on highways) and automated vehicles do not exist yet and leave researchers and practitioners with different challenges. For instance, current test procedures do not suffice for fully automated vehicles, which are supposed to be completely in charge for the driving task and have no driver as a back up. This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics.We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles. The major challenges identified through the interviews and focus groups, enriched by literature on this topic are related to 1) virtual testing and simulation, 2) safety, reliability, and quality, 3) sensors and sensor models, 4) required scenario complexity and amount of test cases, and 5) handover of responsibility between the driver and the vehicle.Comment: 8 page

    Ontology based Scene Creation for the Development of Automated Vehicles

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    The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10 figure

    Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

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    Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strategical choices. Most state-of-the-art AV platforms implement tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives. Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable but have no clear priority against each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation

    Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

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    Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios
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