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

[2312.09436] Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2312.09436 (cs) [Submitted on 27 Nov 2023 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy Authors: Jung-Hoon Cho , Sirui Li , Jeongyun Kim , Cathy Wu View a PDF of the paper titled Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy, by Jung-Hoon Cho and 3 other authors View PDF HTML (experimental) Abstract: The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to maximize vehicle speed and throughput. This paper explores advisory autonomy, in which real-time driving advisories are issued to the human drivers, thus achieving near-term performance of automated vehicles. Due to the complexity of traffic systems, recent studies of coordinating CAVs have resorted to leveraging deep reinforcement learning (RL). Coarse-grained advisory is formalized as zero-order holds, and we consider a range of hold duration from 0.1 to 40 seconds. However, despite the similarity of the higher frequency tasks on CAVs, a direct application of deep RL fails to be generalized to advisory autonomy tasks. To overcome this, we utilize zero-shot transfer, training policies on a set of source tasks--specific traffic scenarios with designated hold durations--and then evaluating the efficacy of these policies on different target tasks. We introduce Temporal Transfer Learning (TTL) algorithms to select source tasks for zero-shot transfer, systematically leveraging the temporal structure to solve the full range of tasks. TTL selects the most suitable source tasks to maximize the performance of the range of tasks. We validate our algorithms on diverse mixed-traffic scenarios, demonstrating that TTL more reliably solves the tasks than baselines. This paper underscores the potential of coarse-grained advisory autonomy with TTL in traffic flow optimization. Comments: 18 pages, 12 figures Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY) Cite as: arXiv:2312.09436 [cs.RO] (or arXiv:2312.09436v3 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2312.09436 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Transactions on Robotics, 2026 Submission history From: Jung-Hoon Cho [ view email ] [v1] Mon, 27 Nov 2023 21:18:06 UTC (2,752 KB) [v2] Thu, 1 Aug 2024 20:19:29 UTC (8,341 KB) [v3] Fri, 10 Apr 2026 03:00:25 UTC (9,227 KB) Full-text links: Access Paper: View a PDF of the paper titled Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy, by Jung-Hoon Cho and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2023-12 Change to browse by: cs cs.AI cs.LG cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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