Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning

Author:

Rosbach Sascha1,Li Xing1,Grosjohann Simon1,Homoceanu Silviu1,Roth Stefan2

Affiliation:

1. Volkswagen AG,Wolfsburg,Germany,38440

2. Technische Universität Darmstadt,Visual Inference Lab,Department of Computer Science,64289,Germany,Darmstadt

Publisher

IEEE

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Risk-aware lane-change trajectory planning with rollover prevention for autonomous light trucks on curved roads;Mechanical Systems and Signal Processing;2024-04

3. Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios;IEEE Transactions on Intelligent Transportation Systems;2023-01

4. Calibration of human driving behavior and preference using vehicle trajectory data;Transportation Research Part C: Emerging Technologies;2022-12

5. AB-Mapper: Attention and BicNet based Multi-agent Path Planning for Dynamic Environment;2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2022-10-23

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