Autonomous driving paper index
A-RoI: Adaptive Region of Interest to Enhance Obstacle Awareness for Autonomous Driving
One-line summary
This paper presents Adaptive Region of Interest (A-RoI), a novel technique that dynamically updates the RoI on a LiDAR HD map based on the vehicle's real-time position and planned trajectory.
Engineering notes
Integrating obstacle positions with the A-RoI within a Bird's Eye View (BEV) framework enables effective obstacle perception, significantly enhancing navigational decision-making.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Autonomous vehicles navigating dynamic environments demand adaptive perception and planning for safe operation. Crucially, navigational decisions rely on understanding the position and behaviour of surrounding objects. Traditional, static Regions of Interest (RoIs) for obstacle detection prove inadequate in meeting these dynamic and context-dependent requirements. This paper presents Adaptive Region of Interest (A-RoI), a novel technique that dynamically updates the RoI on a LiDAR HD map based on the vehicle's real-time position and planned trajectory. Integrating obstacle positions with the A-RoI within a Bird's Eye View (BEV) framework enables effective obstacle perception, significantly enhancing navigational decision-making. Real-time validation on a sensorequipped autonomous vehicle demonstrates the effectiveness of the proposed approach. Future research will explore enhancing A-RoI's adaptability through multi-modal sensor fusion and incorporating learning-based methodologies for further improvements in perception and decision-making capabilities.
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