伴随车联网技术的发展，道路交通流呈现智能网联自动驾驶汽车与传统人工驾驶车辆混合共存发展态势，研究网联新型混合车流换道驾驶行为的风险特性极其重要。基于安全裕度理论，建立了换道行为风险量化模型，采用故障树分析法，推导换道的时间和空间风险，进行时空融合的风险评定量化，以判断车辆是否处于安全变道状态，并动态平衡车辆换道行为可能存在的风险。运用SUMO软件对建立的量化模型进行仿真验证分析，碰撞时间倒数与瞬时风险系数均值分别下降0.1与0.05，同时变化趋势趋于稳定。安全裕度风险量化模型使换道风险得到了有效控制的同时，交通流的稳定性得到了较大提高，可保障未来网联环境中自主驾驶车辆队列的稳态运行，从而提高交通容量和交通效率。

Along with the development of connected vehicle technology, the road traffic flow presents the mixed coexistence development of intelligent networked self-driving vehicles and traditional human-driven vehicles, and it is extremely important to study the risk characteristics of lane change driving behavior of the new networked mixed traffic flow. Based on the safety margin theory, a risk quantification model of lane changing behavior was established, and the fault tree analysis method was used to derive the temporal and spatial risks of lane changing and to quantify the risk assessment of temporal and spatial fusion to determine whether the vehicle was in a safe lane changing state and to provide early warning of the possible risks of vehicle lane changing behavior. The simulation validation analysis of the established quantitative model using SUMO software shows that the mean values of reciprocal of time to collision and instantaneous risk coefficient γ decrease by about 0.1 and 0.05, respectively, while the change trend tends to be stable. The safety margin risk quantification model enables the risk of lane change to be effectively controlled while the stability of traffic flow is greatly improved, which can guarantee the steady-state operation of autonomous vehicles queues in the future net connected environment and thus improve traffic capacity and traffic efficiency.