在密集城市交通中实现自动驾驶车辆的安全运行，依赖于即使车载感知能力下降时仍保持可靠的感知与规划能力。在真实驾驶条件下，摄像头观测常因遮挡、运动模糊、光照变化及传感器噪声而退化；若不加区分地将此类退化观测随时间聚合，则轨迹规划将变得不稳定，从而增加自车及周边道路使用者的碰撞风险。近期的鸟瞰图（BEV）方法通过共享空间表征统一感知与规划，但多数方法在跨帧融合时间信息时未评估底层观测的可靠性。本文提出一种面向自动驾驶的可靠上下文感知与时间规划框架（RCT-AD），显式建模特征质量与时间一致性，以支持更安全、更稳定的规划。其中，可靠上下文感知模块对每帧观测的可靠性进行评分，并通过质量门控的先进后出（FILO）记忆机制选择性保留可信特征，利用可靠的历史上下文重建退化观测，避免退化输入破坏场景表征的稳定性；时间轨迹规划器捕获长期依赖关系与多智能体交互，生成更平滑、更具安全性意识的轨迹；联合检测与分割头则将语义与运动线索注入共享BEV空间，增强场景理解能力。在nuScenes自动驾驶基准上的实验表明，RCT-AD在感知精度、运动预测与规划鲁棒性方面均优于近期端到端基线方法，取得61.5的nuScenes检测分数（NDS）、52.9的平均精度（mAP）以及52.3的平均交并比（mIoU）

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.