Abstract:  Following the recent advances in AI, autonomous driving has gained considerable attention in both academia and industries. For autonomous driving the classical paradigm is to use a hierarchical architecture of perception, planning and control; but recent deep learning progress lets foresee the AI-based approaches as the alternative solutions to the problem. Companies are pushing hard to produce the first fully autonomous self-driving cars. Various approaches ranging from end-to-end deep learning techniques to multi-layer hierarchical architectures are being taken to achieve this goal. In most of the approaches, advanced driving assistance systems (ADAS) play a pivotal role in enhancing the driving intelligence. Our work is mostly focused on the decision-making layer of the ADAS systems. High-level decision making is a critical feature for ADAS, 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.

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