Distributed Data-Sharing Consensus in Cooperative Perception of Autonomous Vehicles
Distributed Data-Sharing Consensus in Cooperative Perception of Autonomous Vehicles
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DOI：
10.1109/icdcs54860.2022.00119
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Chenxi Qiu;Sourabh Yadav;A. Squicciarini;Qing Yang;Song Fu;Juanjuan Zhao;Chengzhong Xu
中科院分区：
文献类型：
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作者：
Chenxi Qiu;Sourabh Yadav;A. Squicciarini;Qing Yang;Song Fu;Juanjuan Zhao;Chengzhong Xu
为了在无人驾驶的情况下实现自动驾驶，自动驾驶汽车需要使用车载传感器来感知周围的障碍物，而这些传感器的感知精度可能会受到自身感知范围的限制。提高车辆感知精度的一种有效方法是让附近的车辆交换传感器数据，从而使车辆能够检测到超出自身感知范围的障碍物，称为合作感知。然而，共享的传感器数据可能会泄露车辆乘客的敏感信息，引发隐私和安全问题（例如跟踪或敏感位置泄露）。在本文中，我们提出了一种新的自动驾驶汽车协同感知数据共享策略，其目标是在不影响感知准确性的前提下最小化车辆信息泄露。考虑到车辆在不同的交通环境下通常有不同的数据共享需求，我们的策略为车辆提供了根据自身需求决定共享哪种类型传感器数据的自主权。此外，考虑到车辆数据共享决策的动态性，可以对政策进行调整，激励车辆决策向期望的决策领域收敛，从而长期维持健康的合作环境。为了实现这一目标，我们利用博弈论模型分析了车辆数据共享决策的动力学，并根据分析结果优化了政策中的数据共享比例。最后，我们进行了广泛的跟踪驱动仿真来测试所提出的数据共享策略的性能。实验结果表明，该策略可以有效地激励车辆的数据共享决策到期望的决策领域。
To enable self-driving without a human driver, an autonomous vehicle needs to perceive its surrounding obstacles using onboard sensors, of which the perception accuracy might be limited by their own sensing range. An effective way to improve vehicles’ perception accuracy is to let nearby vehicles exchange their sensor data so that vehicles can detect obstacles beyond their own sensing ranges, called cooperative perception. The shared sensor data, however, might disclose the sensitive information of vehicles’ passengers, raising privacy and safety concerns (e.g. stalking or sensitive location leakage).In this paper, we propose a new data-sharing policy for the cooperative perception of autonomous vehicles, of which the objective is to minimize vehicles’ information disclosure without compromising their perception accuracy. Considering vehicles usually have different desires for data-sharing under different traffic environments, our policy provides vehicles autonomy to determine what types of sensor data to share based on their own needs. Moreover, given the dynamics of vehicles’ data-sharing decisions, the policy can be adjusted to incentivize vehicles’ decisions to converge to the desired decision field, such that a healthy cooperation environment can be maintained in a long term. To achieve such objectives, we analyze the dynamics of vehicles’ data-sharing decisions by resorting to the game theory model, and optimize the data-sharing ratio in the policy based on the analytic results. Finally, we carry out an extensive trace-driven simulation to test the performance of the proposed data-sharing policy. The experimental results demonstrate that our policy can help incentivize vehicles’ data-sharing decisions to the desired decision fields efficiently and effectively.