用计算机视觉技术提升自动驾驶在施工区的安全避障能力
A Computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone
- 基于YOLO框架构建施工区障碍物检测模型
- 平均精度超94%，推理时间仅1.6毫秒
- 适合自动驾驶安全系统研发人员参考
为建设更智能、更安全的城市，构建安全、高效、可持续的交通系统至关重要。自动驾驶系统（ADS）在智慧交通发展中扮演关键角色，是近年来汽车领域的主要挑战之一。配备自动驾驶系统的车辆具备自适应巡航、碰撞预警、自动泊车等多种先进功能。当前自动驾驶辅助系统（ADAS）研究的核心之一是无论在何种驾驶环境下，都能准确识别施工区域的道路障碍物。本文提出一种创新且高精度的计算机视觉障碍物检测模型，可在施工区激活并适应多种偏移条件，有效降低自动驾驶车辆的风险。该模型基于YOLO框架，平均精度超过94%，在验证数据集上的推理时间仅为1.6毫秒，充分证明了方法在应对道路风险方面的鲁棒性。
原文摘要 · Abstract (English)
To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. The autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. This paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. The model developed with the YOLO framework achieved a mean average precision exceeding 94\% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles.
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