Autonomous agents plan their paths through known and unknown environments to reach their goals. When mu ltiple autonomous agents share the same area, conflict situations may occur that need to be solved. We present a decentralized decision mak ing algorithm to solve conflicts among autonomous agents. It is based on two main ideas: First, we in troduce an innovative operationalization of cooperative behavior which allows to determine whether a behavior is cooperative by computing the total utility and com paring it to a reference utility. Second, we use motion primitives as a representation of available maneuvers obeying individual and environmental restrictions. The decentralized decision making algorithm is based on communication among the autonomous agents to find an optimal maneuver combin ation. Simulations show that our algorithm is applicable to different highway traffic scenarios of two automated vehicles. We use a mean-square acceleration as an individual cost function and show that our intelligent controller leads to coop erative solutions.
We introduce a novel concept based on maneuver templates, which are formalized collaborative maneuvers, to select cooperative driving strategies. The approach is based on the exclusion principle, where we derive provable conditions to discard unsafe cooperative maneuvers for a given traffic situation. We thereby consider the full action set of all cooperative vehicles and do not discretize the action space. We demonstrate the applicability of our approach with numerical examples.
Objective: The Vision Zero initiative pursues the goal of eliminating all traffic fatalities and severe injuries. Today's advanced driver assistance systems (ADAS) are an important part of the strategy toward Vision Zero. In Germany in 2018 more than 26,000 people were killed or severely injured by traffic accidents on motorways and rural roads due to road accidents. Focusing on collision avoidance, a simulative evaluation can be the key to estimating the performance of state-of-theart ADAS and identifying resulting potentials for system improvements and future systems. This project deals with the effectiveness assessment of a combination of ADAS for longitudinal and lateral intervention based on German accident data. Considered systems are adaptive cruise control (ACC), autonomous emergency braking (AEB), and lane keeping support (LKS). Methods: As an approach for benefit estimation of ADAS, the method of prospective effectiveness assessment is applied. Using the software rateEFFECT, a closed-loop simulation is performed on accident scenario data from the German In-Depth Accident Study (GIDAS) precrash matrix (PCM). To enable projection of results, the simulative assessment is amended with detailed single case studies of all treated cases without PCM data. Results: Three categories among today's accidents on German rural roads and motorways are reported in this study: Green, grey, and white spots. Green spots identify accidents that can be avoided by state-of-the-art ADAS ACC, AEB, and LKS. Grey spots contain scenarios that require minor system modifications, such as reducing the activation speed or increasing the steering torque. Scenarios in the white category cannot be addressed by state-of-the-art ADAS. Thus, which situations demand future systems are shown. The proportions of green, grey, and white spots are determined related to the considered data set and projected to the entire GIDAS. Conclusions: This article describes a systematic approach for assessing the effectiveness of ADAS using GIDAS PCM data to be able to project results to Germany. The closed-loop simulation run in rateEFFECT covers ACC, AEB, and LKS as well as relevant sensors for environment recognition and actuators for longitudinal and lateral vehicle control. Identification of green spots evaluates safety benefits of state-of-the-art level 0-2 functions as a baseline for further system improvements to address grey spots. Knowing which accidents could be avoided by standard ADAS helps focus the evolution of future driving functions on white spots and thus aim for Vision Zero.
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