Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles
Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles
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DOI：
10.1109/cvpr52688.2022.01674
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发表时间：
2022-05
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影响因子：
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通讯作者：
Jiaxun Cui;Hang Qiu;Dian Chen;P. Stone;Yuke Zhu
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作者：
Jiaxun Cui;Hang Qiu;Dian Chen;P. Stone;Yuke Zhu
自动驾驶汽车的光学传感器和学习算法在过去几年中取得了巨大的进步。尽管如此，当今自动驾驶汽车的可靠性受到有限的视线传感能力和处理极端情况的数据驱动方法的脆弱性的阻碍。随着通信技术的发展，车对车通信的协同感知已经成为一种很有前途的范例，以加强在危险或紧急情况下的自动驾驶。我们介绍Coopernaut，这是一种端到端的学习模型，它使用跨车辆感知进行基于视觉的协作驾驶。我们的模型将Li-DAR信息编码为紧凑的基于点的表示，这些表示可以通过真实的无线信道在车辆之间传输。为了评估我们的模型，我们开发了Autocastsim，这是一个网络增强的驾驶模拟框架，其中包含易发生事故的场景。我们的Autocastsim上的实验表明，我们的合作感知驾驶模型导致在这些具有挑战性的驾驶情况下，平均成功率比以自我为中心的驾驶模型提高了40%，并且比先前的工作V2 VNet的带宽要求小了5\times $。Cooper-naut和Autocastsim可在https://ut-austin-rpl.github.io/Coopernaut/上获得。
Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today's autonomous vehicles is hindered by the limited line-of-sight sensing capability and the brittleness of data-driven methods in handling extreme situations. With recent developments of telecommunication technologies, cooperative perception with vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency situations. We introduce Coopernaut,an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our model encodes Li-DAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless channels. To evaluate our model, we develop Autocastsim,a network-augmented driving simulation framework with example accident-prone scenarios. Our experiments on Autocastsim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate over egocentric driving mod-els in these challenging driving situations and a $5\times$ smaller bandwidth requirement than prior work V2VNet. Cooper-nautand Autocastsim are available at https://ut-austin-rpl.github.io/Coopernaut/.