Level-$k$ Meta-Learning for Pedestrian-Aware Self-Driving

One challenge for self-driving cars is their interactions not only with other vehicles but also with pedestrians in urban environments. The unpredictability of pedestrian behaviors at intersections can lead to a high rate of accidents. The first pedestrian fatality caused by autonomous vehicles was reported in 2018 when a self-driving Uber vehicle struck a woman crossing an intersection in Tempe, Arizona in the nighttime. There is a need for creating machine intelligence that allows autonomous vehicles to control the car and adapt to different pedestrian behaviors to prevent accidents. In this work, (a) We develop a Level-$k$ Meta Reinforcement Learning model for the vehicle-human interactions and define its solution concept; (b) We test our LK-MRL structure in level-$0$ pedestrians interacting with level-$1$ car scenario, compare the trained policy with multiple baseline methods, and demonstrate its advantage in road safety; (c) Furthermore, based on the properties of level-$k$ thinking, we test our LK-MRL structure in level-$1$ pedestrians interacting with level-$2$ car scenario and verify by experimental results that LK-MRL maintains its advantageous with the using of reinforcement learning of producing different level of agents with strategies of the best response of their lower level thinkers, which provides us possible to create higher level scenarios.