智能汽车拟人驾驶风险量化方法研究

驾驶风险量化评估对智能汽车拟人驾驶决策至关重要,针对复杂多任务场景下的驾驶风险量化问题,提出了一种基于 人类风险感知机理的智能汽车驾驶风险量化方法。 首先,利用传感器获取驾驶场景周围环境信息与行驶状态信息,并根据人类 驾驶经验对潜在冲突因素赋值代价,生成驾驶场景代价地图;其次,根据车辆运动状态与拟人驾驶的基本原则,利用高斯函数建 立动态风险模型;最后,结合驾驶场景代价图与动态风险模型实时计算拟人驾驶风险量化值。 仿真结果表明,提出的方法能够 基于人类驾驶经验,计算出动态变化的驾驶风险量化值,应用于智能汽车自动驾驶决策,可产生拟人驾驶行为。

Research on human-like driving risk quantification method for intelligent vehicles

Conducting a quantize value of driving risk is crucial for the human-like driving decision of intelligent vehicles (IV). Aiming at the challenge of quantifying driving risks in complex multi-task scenarios, a method of driving risk quantification of IV based on human risk perceived mechanism is proposed. By utilizing vehicle or road sensors, measurements of the surrounding environment and state information have been obtained. And a cost map of the driving scene is created by assigning costs to potential collision factors such as roads, plants, and obstacles that the driver believes may occur in the first stage. Based on the fundamental principles of human driving and vehicle motion states, a dynamic risk model is established utilizing Gaussian functions. The real-time computation of driving risk with human-like characteristics is accomplished through the integration of the cost map for the driving environment and a dynamic risk model. The simulation results demonstrate the effectiveness of the proposed method in quantifying dynamic driving risk based on human driving experience, which is applicable to autonomous driving decision-making for IV and capable of generating human-like driving behavior.