A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving

2026-07-06

AI Summary

This work addresses the challenge of unstable planning and increased collision risk in autonomous driving under perceptual degradation—such as occlusion, motion blur, and illumination changes—where observations become unreliable. To this end, the authors propose the RCT-AD framework, which reconstructs degraded observations via a quality-gated FILO memory mechanism and integrates semantic, motion, and multi-agent interaction cues within a shared bird’s-eye-view (BEV) representation. By coupling a reliable context-aware module with an end-to-end temporal trajectory planner, RCT-AD achieves robust perception-planning integration. On the nuScenes benchmark, it significantly outperforms existing end-to-end approaches, attaining 61.5 NDS, 52.9 mAP, and 52.3 mIoU while maintaining computational efficiency suitable for real-time deployment.

Abstract

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.

Problem

• autonomous driving
• perception reliability
• temporal planning
• degraded observations
• trajectory stability

Innovation

• Reliable Context Awareness
• Temporal Planning
• Bird's-Eye-View (BEV)
• Quality-Gated Memory
• End-to-End Autonomous Driving