Design and Implementation of Multi-Sensor Perception System for Autonomous Vehicle

Engineering notes

Key topics: autonomous driving, autonomous vehicle, bev, object detection, lidar, point cloud, carla, deployment, 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

This paper provides an in-depth exploration of a design and implementation of multi-sensor perception system for autonomous vehicle (AV). The proposed system integrates a host laptop runs the CARLA simulator to produce a configurable urban scene with traffic, pedestrians, and virtual sensors, namely six RGB Cameras and a LiDAR (Light Detection and Ranging). The sensor streams are sent via ROS2 middleware, which communicates over TCP/IP to an NVIDIA Jetson AGX Xavier, enabling online perception with reliable communication. Image data are processed with YOLOv8 (You Only Look Once) for object detection and the results are shown as a mosaic that merges the six camera views. LiDAR point clouds are processed using a PointPillars-based pipeline and visualized in a bird’s-eye-view (BEV) format. Experimental results provide real-time visualizations of detected objects and LiDAR spatial structures, supporting the feasibility of the architecture for simulation-based autonomous vehicle research and rapid prototyping of embedded perception systems. Critically, this work demonstrates real-time multi-sensor autonomous vehicle perception on affordable edge hardware, making the technology accessible to startups and research institutions. This represents a fundamental shift in autonomous vehicle deployment and removing traditional barriers.

Links and sources

• Official / arXiv page

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