Adding an SVO-based Bayesian intention estimator to a DQN controller lowers simulated lane-change collision rates to 7.52% and raises success rates to 92.02%.
Lane Change Decision-Making through Deep Reinforcement Learning
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abstract
Due to the complexity and volatility of the traffic environment, decision-making in autonomous driving is a significantly hard problem. In this project, we use a Deep Q-Network, along with rule-based constraints to make lane-changing decision. A safe and efficient lane change behavior may be obtained by combining high-level lateral decision-making with low-level rule-based trajectory monitoring. The agent is anticipated to perform appropriate lane-change maneuvers in a real-world-like udacity simulator after training it for a total of 100 episodes. The results shows that the rule-based DQN performs better than the DQN method. The rule-based DQN achieves a safety rate of 0.8 and average speed of 47 MPH
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Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions
Adding an SVO-based Bayesian intention estimator to a DQN controller lowers simulated lane-change collision rates to 7.52% and raises success rates to 92.02%.