一种用于自动驾驶的可靠上下文感知和时间规划框架
A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving
AI总结：
研究自动驾驶中车载传感退化时的可靠感知与规划问题，提出RCT-AD框架，通过显式建模特征质量和时间一致性，经可靠上下文感知模块和时间轨迹规划器等提升感知、预测及规划能力。
摘要：
自动驾驶车辆在密集城市交通中的安全运行依赖于车载传感退化时仍可靠的感知和规划。实际驾驶中，相机观测常因遮挡、运动模糊等退化，不加区分地聚合会使轨迹规划不稳定。近期鸟瞰图方法未评估观测可靠性。本文提出RCT-AD框架，显式建模特征质量和时间一致性，经可靠上下文感知模块和时间轨迹规划器等提升性能，实验表明其优于近期端到端基线。
英文摘要：
Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that remain reliable when onboard sensing is degraded. In real driving conditions, camera observations are frequently corrupted by occlusion, motion blur, illumination change, and sensor noise, and when such degraded observations are aggregated indiscriminately over time, trajectory planning becomes unstable and collision risk rises for both the ego vehicle and surrounding road users. Recent Bird's-Eye-View (BEV) approaches unify perception and planning through a shared spatial representation, but most fuse temporal information across frames without assessing the reliability of the underlying observations. We present a Reliable Context-Aware and Temporal Planning framework for Autonomous Driving (RCT-AD) that explicitly models feature quality and temporal consistency to support safer, more consistent planning. A Reliable Context Awareness module scores per-frame reliability and selectively retains trustworthy features through a quality-gated First-In-Last-Out (FILO) memory mechanism, reconstructing degraded observations from reliable historical context so that corrupted inputs do not destabilize the scene representation. A Temporal Trajectory Planner captures long-term dependencies and multi-agent interactions to produce smoother, safety-aware trajectories, while a joint detection-and-segmentation head injects semantic and motion cues into the shared BEV space to strengthen scene understanding. Experiments on the nuScenes autonomous driving benchmark show that RCT-AD improves perception accuracy, motion prediction, and planning robustness over recent end-to-end baselines, achieving 61.5 nuScenes Detection Score, 52.9 mean Average Precision, and 52.3 mean Intersection over Union, while maintaining competitive computational efficiency suitable for real-time deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
