← Search

Zejie Jiang

2 accepted papers

2026

BEV-OSP: Obstacle State Prediction in Bird's-Eye View to Enable Obstacle Avoidance and Navigation in Dynamic Environments

RA-L 2026

Despite the prevalence of deep reinforcement learning (DRL) for navigation in dynamic environments, existing end-to-end DRL methods still struggle to effectively balance global planning with local obstacle avoidance and to perceive the dynamics of obstacles. Meanwhile, external detection methods are

Cited by 0SourceScholar
2026

DARE to Navigate: Spatiotemporal Dynamic Augmented LiDAR Encoder for Obstacle Detection and Avoidance in Crowded Environments

RA-L 2026

Navigating in environments with both static obstacles and dense crowds of pedestrians using low-cost sensors and lightweight controllers is crucial for the widespread application of mobile robots, where the key challenge is the localization and tracking of dynamic obstacles using only sensor reading

Cited by 0SourceScholar