To enhance on-road environmental perception for autonomous driving, accurate and real-time analytics on high-resolution video frames generated from on-board cameras be-comes crucial. In this paper, we design a lightweight object location method based on class activation mapping (CAM) to rapidly capture the region of interest (RoI) boxes that contain driving safety related objects from on-board cameras, which can not only improve the inference accuracy of vision tasks, but also reduce the amount of transmitted data. Considering the limited on-board computation resources, the RoI boxes extracted from the raw image are offloaded to the edge for further processing. Considering both the dynamics of vehicle-to-edge communications and the limited edge resources, we propose an adaptive RoI box offloading algorithm to ensure prompt and accurate inference by adjusting the down-sampling rate of each box. Extensive experimental results on four high-resolution video streams demonstrate that our approach can effectively improve the overall accuracy by up to 16% and reduce the transmission demand by up to 49%, compared with other benchmarks.

翻译：为提升自动驾驶中的道路环境感知能力，对车载摄像头生成的高分辨率视频帧进行精确且实时的分析至关重要。本文基于类激活映射（CAM）设计了一种轻量化目标定位方法，用于快速从车载摄像头中提取包含驾驶安全相关目标的感兴趣区域（RoI）框。该方法不仅能提升视觉任务的推理精度，还能减少传输数据量。鉴于车载计算资源有限，从原始图像中提取的RoI框被卸载至边缘端进行进一步处理。同时考虑车辆与边缘通信链路的动态特性及边缘资源受限问题，我们提出一种自适应RoI框卸载算法，通过调整每个框的下采样率来确保推理的即时性与准确性。在四个高分辨率视频流上的大量实验结果表明，与其他基准方法相比，本方法可将整体精度提升最高16%，并将传输需求降低最高49%。