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The paper addresses the challenge of automated highway merging by formulating the speed planning problem as a deterministic Markov Decision Process (MDP). They use jerk as the action space to ensure smooth acceleration profiles, mitigating abrupt changes. The proposed algorithm is validated through both simulations and real-world experiments, demonstrating its effectiveness compared to a baseline Intelligent Driver Model (IDM) approach.
Modeling highway merging as a deterministic MDP allows for robust speed optimization in autonomous vehicles, outperforming traditional methods in both simulation and real-world tests.
This study presents a robust optimization algorithm for automated highway merge. The merging scenario is one of the challenging scenes in automated driving, because it requires adjusting ego vehicle's speed to match other vehicles before reaching the end point. Then, we model the speed planning problem as a deterministic Markov decision process. The proposed scheme is able to compute each state value of the process and reliably derive the optimal sequence of actions. In our approach, we adopt jerk as the action of the process to prevent a sudden change of acceleration. However, since this expands the state space, we also consider ways to achieve a real-time operation. We compared our scheme with a simple algorithm with the Intelligent Driver Model. We not only evaluated the scheme in a simulation environment but also conduct a real world testing.
