  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.

Robotics