Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University Road #2, Nanjing, 211189, China.
Accid Anal Prev. 2020 Dec;148:105805. doi: 10.1016/j.aap.2020.105805. Epub 2020 Oct 24.
Benefiting from the rapid development of communication and intelligent vehicle technology in recent years, most traffic information is capable of being collected, processed, and transmitted to each vehicle through a connected and automated vehicles (CAVs) system. To meet the higher requirements of driving safety in CAVs environment, it is necessary to develop more effective safety evaluation indicators that combine all the traffic information received by the vehicle. To this end, this study proposes a novel methodology for risk perception and warning strategy based on safety potential field model to minimize driving risk in the CAVs environment. A dynamic safety potential field model was constructed to describe the spatial distribution of driving risk encountered by vehicles. This safety potential field model can comprehensively consider the impact of various types of traffic information on driving risk. And then, a novel driving risk indicator, named potential field indicator (PFI), was established to evaluate the level of driving risk. Finally, an early warning strategy was proposed to prevent accidents, whose performance was evaluated by several simulations carried out through SUMO simulator. The comparison with some classic risk indicators indicate that our proposed PFI can more accurately reflect the actual driving risk faced by vehicles under different vehicle motion states and thus is more suitable for driving risk assessment in the CAVs environment. It is expected that the findings in this study could be valuable in improving the performance of strategic decision-making in driver assistance systems in the CAVs environment.
受益于近年来通信和智能车辆技术的快速发展，大多数交通信息都能够通过车联网系统进行收集、处理和传输到每辆车。为了满足车联网环境下更高的驾驶安全要求，有必要开发更有效的安全评估指标，将车辆接收到的所有交通信息结合起来。为此，本研究提出了一种基于安全势场模型的风险感知和预警策略的新方法，以最小化车联网环境下的驾驶风险。构建了一个动态安全势场模型来描述车辆遇到的驾驶风险的空间分布。该安全势场模型可以综合考虑各种类型的交通信息对驾驶风险的影响。然后，建立了一种新的驾驶风险指标，命名为势场指标（PFI），用于评估驾驶风险水平。最后，提出了一种预警策略来预防事故，通过 SUMO 模拟器进行的几次模拟评估了其性能。与一些经典风险指标的比较表明，我们提出的 PFI 可以更准确地反映车辆在不同车辆运动状态下实际面临的驾驶风险，因此更适合车联网环境下的驾驶风险评估。预计本研究的结果将有助于提高车联网环境下驾驶员辅助系统战略决策的性能。
