Addressing Lane Keeping and Intersections using Deep Conditional Reinforcement Learning

Author(s)

Silva, Vitor A. S. ; Grassi Jr, Valdir ; DaSilva, BMF ; Todt, E ; Nascimento, TP ; DosSantos, DH ; Curvelo, CDF ; Fabro, JA

• Document type: Journal article
• Source: 2021 LATIN AMERICAN ROBOTICS SYMPOSIUM / 2021 BRAZILIAN SYMPOSIUM ON ROBOTICS / 2021 WORKSHOP OF ROBOTICS IN EDUCATION (LARS-SBR-WRE 2021); v. N/A, p. 6-pg., 2021-01-01.

Abstract

End-to-end deep reinforcement learning (DRL) methods have been largely used to solve self-driving tasks. However, they usually deal only with simple problems, such as lane keeping. When it is needed to solve more complex tasks, traditional DRL algorithms fails. In this work we combine Conditional Learning and Proximal Policy Optimization (PPO) to solve the problem of turning at intersection and lane keeping in an end-to-end fashion. In our approach we trained three PPO sub-policies to perform right and left turns and follow lane. The sub-policies are activated once at a time according to a command sent by the local planner. We also used three different image transformation to verify their impact on learning speed and generalization. Experiments were conducted in an urban scenario composed by several intersections of type T on CARLA Simulator. Results show that our approach is feasible and presents good performance on accomplishing the goal. Moreover, we could confirm that properly choosing an image transformation can improve sample efficiency and generalization capability. (AU)

• FAPESP's process: 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment
• Grantee: Marco Henrique Terra
• Support Opportunities: Research Projects - Thematic Grants