This work explores the design of a central collaborative driving strategy between connected cars with the objective of improving road safety in case of highway on-ramp merging scenario. Based on a suitable method for predicting vehicle motion and behavior for a central collaborative strategy, a dynamic Bayesian network method that predicts the intention of drivers in highway on-ramp is proposed. The method was validated using real data of detailed vehicle trajectories on a segment of interstate 80 in Emeryville, California.
High-speed highway on-ramp merging is one of the most difficult and critical tasks for any autonomous driving system. This work studies this problem by combining deep deterministic policy gradient (DDPG) reinforcement learning with drivers’ intentions prediction. Our proposed solution is based on an artificial neural network to predict drivers’ intentions, used as an input state to the DDPG agent that outputs the longitudinal acceleration to the merging vehicle. We show that this solution improves safety performances.
This paper proposes a new approach for predicting drivers' intentions in a Highway on-ramp merge situation using a central road side unit (RSU) with probabilistic classifiers.
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