With the development of key technologies like environment perception, the automation level of autonomous vehicles has been increasing. However, before reaching highly autonomous driving, manual driving still needs to participate in the driving process to ensure the safety of human-vehicle shared driving. The existing human-vehicle cooperative driving focuses on auto engineering and drivers' behaviors, with few research studies in the field of visual perception. Due to the bad performance in the complex road traffic conflict scenarios, cooperative visual perception needs to be studied further. In addition, the autonomous driving perception system cannot correctly understand the characteristics of manual driving. Based on the background above, this paper directly proposes a human-vehicle cooperative visual perception method to enhance the visual perception ability of shared autonomous driving based on the transfer learning method and the image fusion algorithm for the complex road traffic scenarios. Based on transfer learning, the mAP of object detection reaches 75.52% and lays a solid foundation for visual fusion. And the fusion experiment further reveals that human-vehicle cooperative visual perception reflects the riskiest zone and predicts the conflict object's trajectory more precisely. This study pioneers a cooperative visual perception solution for shared autonomous driving and experiments in real-world complex traffic conflict scenarios, which can better support the following planning and controlling and improve the safety of autonomous vehicles.
翻译:随着环境观念等关键技术的发展,自动驾驶车辆的自动化水平一直在不断提高;然而,在达到高度自主驾驶之前,人工驾驶仍需要参与驾驶过程,以确保车辆共用驾驶的安全;现有的载人车辆合作驾驶侧重于汽车工程和驾驶者行为,在视觉观念领域几乎没有研究;由于复杂的道路交通冲突情景表现不佳,需要进一步研究合作视觉观念;此外,自主驾驶视觉认识系统不能正确理解手动驾驶的特点;根据上述背景,本文直接建议采用载人车辆合作视觉认识方法,以提高根据转移学习方法和复杂道路交通情况图像集成算法进行的共同自主驾驶的视觉认识能力;根据转移学习,物体探测MAP达到75.52%,为视觉融合打下坚实的基础;此外,融合实验还进一步表明,人类-车辆合作视觉认识反映最危险的区域,并更准确地预测了冲突物体的轨迹;这一研究开创了一种合作视觉认识解决方案,以共同自主驾驶和实验为基础,以转移学习方法为基础,提高共同自主驾驶能力;根据转移学习结果,改进了复杂的道路交通状况的图像集成算法;根据转移学习,可以更好地支持对现实复杂的交通进行安全规划,改进。