A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving
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 o...
| Authors | Argho Dey, Yunfei Yin, Swachha Ray et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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