Resource Efficient Driving Policy

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When attacking the problem of Autonomous Driving, one must take into account strict computational constraints, posed by the desired low cost of sensors and processors, and by the required real-time performance. Specifically, when considering Driving Policy, many of the current state-of-the-art solutions for planning in large state spaces (applied to different problems), are ruled out. We discuss approaches which allow feasible planning, through different representations of the state space, along with the use of both supervised and reinforcement learning algorithms.



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