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Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy by Cho, Jung-Hoon; Li, Sirui; Kim, Jeongyun; Wu, Cathy is a scholarly article available to read on EtoBox.

What is Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy about?

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

Author

Cho, Jung-Hoon; Li, Sirui; Kim, Jeongyun; Wu, Cathy

Published

2023

Language

EN