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• W2904339943 abstract "Ensuring the safety of self-driving vehicles is a challenging task, especially if other traffic participants severely deviate from the predicted behavior. One solution is to ensure that the vehicle is able to execute a collision-free evasive trajectory at any time. However, a fast method to plan these socalled fail-safe trajectories does not yet exist. Our new approach plans fail-safe trajectories in arbitrary traffic scenarios by incorporating convex optimization techniques. By integrating safety verification in the planner, we are able to generate fail-safe trajectories in real-time, which are guaranteed to be safe. At the same time, we minimize jerk to provide enhanced comfort for passengers. The proposed benefits are demonstrated in different urban and highway scenarios using the CommonRoad benchmark suite and compared to a widely-used sampling-based planner." @default.
• W2904339943 created "2018-12-22" @default.
• W2904339943 creator A5005383495 @default.
• W2904339943 creator A5005479279 @default.
• W2904339943 date "2018-11-01" @default.
• W2904339943 modified "2023-10-01" @default.
• W2904339943 title "Computationally Efficient Fail-safe Trajectory Planning for Self-driving Vehicles Using Convex Optimization" @default.
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• W2904339943 doi "https://doi.org/10.1109/itsc.2018.8569425" @default.
• W2904339943 hasPublicationYear "2018" @default.
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