A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving
2026-07-06 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors address the problem of unreliable camera data hurting self-driving car safety in busy city traffic. They created a new system called RCT-AD that checks how trustworthy incoming data is and uses only the good parts to make better decisions over time. Their approach combines understanding the scene and planning the car’s movements using a special memory method and a detailed trajectory planner. Tests show their system improves perception and planning accuracy while running fast enough for real-time use.
Autonomous VehiclesBird's-Eye-View (BEV)Trajectory PlanningTemporal ConsistencyFeature ReliabilityMotion PredictionSemantic SegmentationnuScenes BenchmarkFirst-In-Last-Out Memory
Authors
Argho Dey, Yunfei Yin, Swachha Ray, Md Minhazul Islam, Zheng Yuan, Sijing Xiong, Hongyu Liu, Zhiqiu Huang
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.