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• W4376632841 abstract "Self-driving vehicles (SDVs) are becoming reality but still suffer from long-tail challenges during natural driving: the SDVs will continually encounter rare, safety-critical cases that may not be included in the dataset they were trained. Some safety-assurance planners solve this problem by being conservative in all possible cases, which may significantly affect driving mobility. To this end, this work proposes a method to automatically adjust the conservative level according to each case's long-tail rate, named dynamically conservative planner (DCP). We first define the long-tail rate as an SDV's confidence to pass a driving case. The rate indicates the probability of safe-critical events and is estimated using the statistics bootstrapped method with historical data. Then, a reinforcement learning-based planner is designed to contain candidate policies with different conservative levels. The final policy is optimized based on the estimated long-tail rate. In this way, the DCP is designed to automatically adjust to be more conservative in low-confidence long-tail cases while keeping efficient otherwise. The DCP is evaluated in the CARLA simulator using driving cases with long-tail distributed training data. The results show that the DCP can accurately estimate the long-tail rate to identify potential risks. Based on the rate, the DCP automatically avoids potential collisions in long-tail cases using conservative decisions while not affecting the average velocity in other typical cases. Thus, the DCP is safer and more efficient than the baselines with fixed conservative levels, e.g., an always conservative planner. This work provides a technique to guarantee SDV's performance in unexpected driving cases without resorting to a global conservative setting, which contributes to solving the long-tail problem practically." @default.
• W4376632841 created "2023-05-17" @default.
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• W4376632841 date "2023-05-12" @default.
• W4376632841 modified "2023-09-24" @default.
• W4376632841 title "Dynamically Conservative Self-Driving Planner for Long-Tail Cases" @default.
• W4376632841 doi "https://doi.org/10.48550/arxiv.2305.07497" @default.
• W4376632841 hasPublicationYear "2023" @default.
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