Understanding the dynamic characteristics of surrounding vehicles and estimating the potential risk of mixed traffic can help reliable autonomous driving. However, the existing risk assessment methods are challenging to detect dangerous situations in advance and tackle the uncertainty of mixed traffic. In this paper, we propose a probabilistic driving risk assessment framework based on intention identification and risk assessment of surrounding vehicles. Firstly, we set up an intention identification model (IIM) via long short-term memory (LSTM) networks to identify the intention possibility of the surrounding vehicles. Then a risk assessment model (RAM) based on the driving safety field is employed to output the potential risk. Specifically, driving safety field can reflect the coupling relationship of drivers, vehicles, and roads by analyzing their interaction. Finally, an integrated risk evaluation model combining both IIM and RAM is developed to form a dynamic potential risk map considering multi-vehicle interaction. For example, in a typical but challenging lane-changing scenario, an intelligent vehicle can assess its driving status by calculating a risk map in real time that represents the risk generated by the estimated intentions of surrounding vehicles. Furthermore, simulations and naturalistic driving experiments are conducted in the extracted lane-changing scenarios, and the results verify the effectiveness of the proposed model considering lane-changing behavior interaction.

了解周围车辆的动态特性并估计混合交通的潜在风险可以帮助可靠的自动驾驶。然而，现有的风险评估方法对于提前发现危险情况和解决混合交通的不确定性具有挑战性。在本文中，我们提出了一种基于意图识别和周围车辆风险评估的概率驾驶风险评估框架。首先，我们通过长短期记忆（LSTM）网络建立了意图识别模型（IIM），以识别周围车辆的意图可能性。然后，基于驾驶安全领域的风险评估模型（RAM）被用于输出潜在风险。具体而言，行驶安全领域可以反映驾驶员，车辆，和道路通过分析它们之间的相互作用。最后，结合IIM和RAM的综合风险评估模型被开发出来，以形成考虑多车交互作用的动态潜在风险图。例如，在典型的但具有挑战性的变道场景中，智能车辆可以通过实时计算风险图来评估其驾驶状态，该风险图表示由周围车辆的估计意图产生的风险。此外，在提取的换道场景中进行了仿真和自然驾驶实验，结果验证了考虑换道行为相互作用的模型的有效性。例如，在典型的但具有挑战性的变道场景中，智能车辆可以通过实时计算风险图来评估其驾驶状态，该风险图表示由周围车辆的估计意图产生的风险。此外，在提取的换道场景中进行了仿真和自然驾驶实验，结果验证了考虑换道行为相互作用的模型的有效性。例如，在典型的但具有挑战性的变道场景中，智能车辆可以通过实时计算风险图来评估其驾驶状态，该风险图表示由周围车辆的估计意图产生的风险。此外，在提取的换道场景中进行了仿真和自然驾驶实验，结果验证了考虑换道行为相互作用的模型的有效性。