CARLA中自动驾驶的模仿学习
Imitation Learning for Autonomous Driving in CARLA
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中文总结 AI 辅助
本研究在CARLA模拟器中通过行为克隆训练紧凑多模态策略，实现无碰撞的闭环自动驾驶，并验证了跨城镇迁移与轨迹恢复能力。
中文摘要 AI 辅助
行为克隆在专家演示上离线训练策略，但部署是闭环的：每个动作都会影响策略接下来接收的观测。我们研究了在CARLA模拟器中，一个紧凑的多模态策略能从离线演示中获取多少闭环驾驶能力。该策略使用五帧历史的RGB图像、激光雷达、车辆遥测和车道航点，以20赫兹的频率预测油门、刹车和转向。演示分三个阶段收集，最后采用系统化的路线生成程序，该程序枚举生成点和可行操作，并验证完成的自动驾驶路线。发布的136万参数策略在236,882个窗口上训练，这些窗口代表来自448次采集的约3.3小时驾驶数据。由此产生的策略在训练路线和保留路线上能自主驾驶数小时。在我们的运行中，它没有发生碰撞，并且还定性地迁移到了一个具有不同道路几何形状的未见过的CARLA城镇。我们还观察到从大的轨迹偏差中恢复的能力，尽管我们不声称在没有受控评估的情况下能系统恢复。我们报告了离线指标，并将测量结果与定性闭环观察区分开来。我们发布了代码、训练好的检查点、ONNX模型、数据样本以及所报告声明的证据审计。
英文摘要
Behavioral cloning trains a policy offline on expert demonstrations, but deployment is closed loop: each action affects the observations the policy receives next. We study how much closed-loop driving competence a compact multimodal policy can acquire from offline demonstrations in the CARLA simulator. The policy uses five-frame histories of RGB images, LiDAR, vehicle telemetry, and lane waypoints to predict throttle, brake, and steering at 20 Hz. Demonstrations were collected in three stages, ending with a systematic route-generation procedure that enumerates spawn points and feasible maneuvers and verifies completed autopilot routes. The released 1.36 million parameter policy was trained on 236,882 windows, representing about 3.3 hours of driving from 448 captures. The resulting policy drives autonomously for hours on training and held-out routes. In our runs, it did so without collisions and also transferred qualitatively to an unseen CARLA town with different road geometry. We also observed recovery from large trajectory deviations, although we do not claim systematic recovery without controlled evaluation. We report offline metrics and distinguish measured results from qualitative closed-loop observations. We release the code, trained checkpoint, ONNX model, data sample, and an evidence audit for the reported claims.
