Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

2019/09/18 by Ali Alizadeh, Majid M. Moghaddam, Alizadeh, Ali +9 · 2 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #Robotics (cs.RO) #Systems and Control (eess.SY) #Traffic control and management #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1909.11538

openalex publication_date 2019/09/18 · openalex created_date 2019/10/03 · openalex updated_date 2026/07/28

Abstract

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.

Citations

• Congested traffic states in empirical observations and microscopic simulations
• Dueling Network Architectures for Deep Reinforcement Learning
• End to End Learning for Self-Driving Cars

Cited by

• Learning to Drive Safely with Hybrid Options
• Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving

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