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arXiv Detail & Related papers (2026-09-08T18:10:30Z)
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arXiv Detail & Related papers (2026-05-18T08:45:24Z)
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arXiv Detail & Related papers (2026-05-13T23:35:14Z)
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arXiv Detail & Related papers (2026-04-06T01:54:41Z)
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arXiv Detail & Related papers (2023-12-16T06:08:53Z)
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving [10.191916541924813]
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arXiv Detail & Related papers (2023-09-14T15:54:56Z)
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arXiv Detail & Related papers (2023-03-07T18:29:15Z)
A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem [9.655068751758952]
Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming. There are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. This paper proposes a hyper-heuristic approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning.
arXiv Detail & Related papers (2022-06-07T11:10:38Z)
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arXiv Detail & Related papers (2021-09-23T22:26:52Z)
An electric vehicle charging station access equilibrium model with M/D/C queueing [0.0]
We propose an EV-to-charging station user equilibrium (UE) assignment model with a M/D/C queue approximation as a nondifferentiable nonlinear program. The model is applied to the large-scale case study of New York City Department of Citywide Administrative Services (NYC DCAS) fleet and EV charging station configuration as of July 8, 2020. Results suggest a policy based on selecting locations with high utilization ratio instead of with high queue delay.
arXiv Detail & Related papers (2021-02-11T05:23:36Z)
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings [129.80279257258098]
Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous. We propose a "safety-critical adaptation" task setting: an agent first trains in non-safety-critical "source" environments. We propose a solution approach, CARL, that builds on the intuition that prior experience in diverse environments equips an agent to estimate risk.
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Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [104.04999499189402]
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment. We propose an uncertainty-aware planning method, called emphrobust imitative planning (RIP) Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. We introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.
arXiv Detail & Related papers (2020-06-26T11:07:32Z)

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