Autonomous discovery of traffic laws with AI traffic scientists
arXiv:2604.05535v1 Announce Type: new Abstract: Traffic signal control TSC requires strategies that are both effective and interpretable for deployment, yet reinforcement learning
arXiv:2604.27753v1 Announce Type: new Abstract: This article outlines a new framework of traffic light optimization through a digital twin of the transport infrastructure, managed
arXiv:2603.18266v1 Announce Type: cross Abstract: Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the
arXiv:2607.03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical
arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full
arXiv:2606.26460v1 Announce Type: new Abstract: AI-based scientific automation is increasingly possible by using agents to generate hypotheses, design experiments, and analyze
arXiv:2603.25328v1 Announce Type: new Abstract: Automated Vehicle (AV) control in mixed traffic, where AVs coexist with human-driven vehicles, poses significant challenges in
arXiv:2610.04088v1 Announce Type: new Abstract: Before autonomous driving systems can be deployed on public roads, it is vital that these systems comply with safety standards,
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