Sensor Fusion in Autonomous Vehicle Decision-Making

Engineering notes

Key topics: autonomous driving, autonomous vehicle, object detection, lidar, sensor fusion, adas, radar, perception. See the paper for implementation details and experimental results.

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

The core component of Advanced Driver Assistance Systems (ADAS) is the perception module, which has been a primary focus for enhancing robustness and quality against various environmental conditions like changing lighting and weather. Recent studies have highlighted sensor fusion, particularly between cameras and LiDAR. This research delves into a less explored domain, focusing on early fusion between camera modules and LiDAR sensors. Employing a deep learning architecture, we aim to integrate minimally processed radar signals and corresponding camera frames to mitigate inaccuracies in the perception module. Our evaluation, conducted using real- world data, demonstrates that combining radar and camera signals can reduce model errors by up to 15% in tasks related to object detection.

Links and sources

• Official / arXiv page

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