Developing reliable autonomous driving algorithms poses challenges in testing, particularly when it comes to safety-critical traffic scenarios involving pedestrians. An open question is how to simulate rare events, not necessarily found in autonomous driving datasets or scripted simulations, but which can occur in testing, and, in the end may lead to severe pedestrian related accidents. This paper presents a method for designing a suicidal pedestrian agent within the CARLA simulator, enabling the automatic generation of traffic scenarios for testing safety of autonomous vehicles (AVs) in dangerous situations with pedestrians. The pedestrian is modeled as a reinforcement learning (RL) agent with two custom reward functions that allow the agent to either arbitrarily or with high velocity to collide with the AV. Instead of significantly constraining the initial locations and the pedestrian behavior, we allow the pedestrian and autonomous car to be placed anywhere in the environment and the pedestrian to roam freely to generate diverse scenarios. To assess the performance of the suicidal pedestrian and the target vehicle during testing, we propose three collision-oriented evaluation metrics. Experimental results involving two state-of-the-art autonomous driving algorithms trained end-to-end with imitation learning from sensor data demonstrate the effectiveness of the suicidal pedestrian in identifying decision errors made by autonomous vehicles controlled by the algorithms.

翻译：开发可靠的自动驾驶算法在测试中面临挑战，尤其是涉及行人的安全关键交通场景。一个悬而未决的问题是如何模拟罕见事件——这些事件未必存在于自动驾驶数据集或脚本化仿真中，但可能在实际测试中出现，最终导致严重的行人相关事故。本文提出一种在 CARLA 仿真器中设计自杀式行人智能体的方法，能够自动生成测试场景，用于检验自动驾驶车辆（AV）在涉及行人的危险情境下的安全性。该行人被建模为强化学习（RL）智能体，配备两种自定义奖励函数，使其能够任意或以高速与自动驾驶车辆发生碰撞。我们未对初始位置和行人行为施加显著约束，而是允许行人和自动驾驶车辆部署在环境中的任意位置，且行人可自由漫游，从而生成多样化的场景。为评估测试过程中自杀式行人与目标车辆的性能，我们提出了三种面向碰撞的评估指标。实验涉及两种基于传感器数据、通过端到端模仿学习训练的先进自动驾驶算法，结果表明自杀式行人在识别这些算法控制的自动驾驶车辆所犯决策错误方面具有有效性。