用自车状态时序信息提升多模态自动驾驶的路径预测能力
METDrive: Multi-modal End-to-end Autonomous Driving with Temporal Guidance
- 融合感知几何特征与自车时序状态，实现联合引导
- 在CARLA上达70%驾驶得分、94%路线完成率
- 适合关注端到端自动驾驶时序建模的研究者
近期研究显示，多模态端到端自动驾驶系统在提升环境理解方面取得显著进展。本文提出METDrive，一种利用自车状态时序特征（包括旋转角度、转向、油门信号和航点向量）进行时序引导的端到端系统。感知传感器数据的几何特征与自车状态的时间序列特征共同通过所提出的时序引导损失函数指导航点预测。在CARLA排行榜基准测试中，METDrive取得了70%的驾驶得分、94%的路线完成率和0.78的违规分数。
原文摘要 · Abstract (English)
Multi-modal end-to-end autonomous driving has shown promising advancements in recent work. By embedding more modalities into end-to-end networks, the system's understanding of both static and dynamic aspects of the driving environment is enhanced, thereby improving the safety of autonomous driving. In this paper, we introduce METDrive, an end-to-end system that leverages temporal guidance from the embedded time series features of ego states, including rotation angles, steering, throttle signals, and waypoint vectors. The geometric features derived from perception sensor data and the time series features of ego state data jointly guide the waypoint prediction with the proposed temporal guidance loss function. We evaluated METDrive on the CARLA leaderboard benchmarks, achieving a driving score of 70%, a route completion score of 94%, and an infraction score of 0.78.
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