An Optimized Edge Platform for 3D Object Detection in Autonomous Driving

Engineering notes

Key topics: autonomous driving, 3d object detection, object detection, lidar, point cloud. 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

Autonomous driving technology has been developed to enhance the safety of vehicles and drivers by recognizing surrounding objects and making appropriate situational judgments using sophisticated sensors. In particular, 3D object detection networks utilizing LiDAR within deep learning models have advanced to achieve accurate object detection. However, performing 3D object detection using LiDAR and advanced sensors requires processing a massive amount of data, making it challenging to handle directly within the vehicle. While external computational resources such as cloud servers can be employed, this approach introduces difficulties in guaranteeing low data exchange latency and ensuring data stability and security. In this paper, we propose a method to process 3D object detection on an edge platform, enabling it to be handled locally for autonomous driving applications. The proposed edge platform consists of a hardware architecture optimized for processing large volumes of point cloud data and a quantized, lightweight network. This enables the edge platform to handle the entire process of acquiring point cloud data via LiDAR, performing 3D object detection, and executing image post-processing tasks directly on the device.

Links and sources

• Official / arXiv page

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