Learning faster to perform autonomous lane changes by constructing maneuvers from shielded semantic actions
Paper i proceeding, 2019

This paper introduces a new method to solve tactical decision making problems for highway lane changes. In the system design, reference sets for low level controllers are employed to formulate semantic meaningful actions used by reinforcement learning algorithm. Safety is ensured by preemptively shielding the Markov decision process (MDP) from unsafe actions. This frees the agent to focus on learning how to interact efficiently with the surrounding traffic. By introducing human demonstration with supervised loss as better exploration strategy, the learning process and initial performance are boosted further. © 2019 IEEE.


Dapeng Liu

AI Sweden

Zenuity AB

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Mattias Brännström

Zenuity AB

Andrew Backhouse

Zenuity AB

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

1838-1844 8917221


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