别惹上麻烦!自动驾驶汽车的风险意识决策

Kasra Mokhtari, Alan R. Wagner
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引用次数: 0

摘要

传统上,风险被描述为预期出现不良结果的可能性,例如自动驾驶汽车的碰撞。准确预测风险或潜在危险情况对于自动驾驶汽车的安全运行至关重要。这项工作结合了使用经过训练的控制器在人群中导航,以及基于风险的自动驾驶车辆决策框架,该框架将基于风险的高级路径规划与基于强化学习的低级控制相结合。我们使用高保真仿真环境评估了我们的方法。我们表明,我们的方法与行人零碰撞,并在大约72%的遍历中预测出风险最小的路径、旅行时间或旅行天数。这项工作可以提高安全性,使自动驾驶汽车有一天能够避开危险情况并做出反应。
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Don’t Get into Trouble! Risk-aware Decision-Making for Autonomous Vehicles
Risk is traditionally described as the expected likelihood of an undesirable outcome, such as a collision for an autonomous vehicle. Accurately predicting risk or potentially risky situations is critical for the safe operation of an autonomous vehicle. This work combines use of a controller trained to navigate around individuals in a crowd and a risk-based decision-making framework for an autonomous vehicle that integrates high-level risk-based path planning with a reinforcement learning-based low-level control. We evaluated our method using a high-fidelity simulation environment. We show our method results in zero collisions with pedestrians and predicted the least risky path, time to travel, or day to travel in approximately 72% of traversals. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.
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