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Towards Co-operative Congestion Mitigation

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arxiv 2302.09140 v1 pith:2RCVDMTY submitted 2023-02-17 cs.LG cs.HCcs.RO

Towards Co-operative Congestion Mitigation

classification cs.LG cs.HCcs.RO
keywords congestionframeworkpoliciesdrivingconstantevaluatehumanmitigate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The effects of traffic congestion are widespread and are an impedance to everyday life. Piecewise constant driving policies have shown promise in helping mitigate traffic congestion in simulation environments. However, no works currently test these policies in situations involving real human users. Thus, we propose to evaluate these policies through the use of a shared control framework in a collaborative experiment with the human driver and the driving policy aiming to co-operatively mitigate congestion. We intend to use the CARLA simulator alongside the Flow framework to conduct user studies to evaluate the affect of piecewise constant driving policies. As such, we present our in-progress work in building our framework and discuss our proposed plan on evaluating this framework through a human-in-the-loop simulation user study.

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Cited by 1 Pith paper

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  1. Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy

    cs.RO 2023-11 unverdicted novelty 7.0

    Temporal Transfer Learning selects source tasks for zero-shot transfer of RL policies to solve a range of coarse-grained advisory autonomy hold durations in traffic optimization more reliably than baselines.