Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Summary: arXiv:2312.09436v3 Announce Type: replace-cross
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 human drivers, thus achieving near-term performance akin to that of automated vehicles.
Introduction
Due to the complexity of traffic systems, recent studies focusing on the coordination of CAVs have resorted to leveraging deep reinforcement learning (RL). This approach has shown promise in optimizing traffic flow; however, its application to advisory autonomy tasks has not yielded consistent results. This raises the need for alternative methodologies that can more effectively harness the advantages of CAV technologies.
Coarse-grained Advisory Autonomy
In this study, we formalize coarse-grained advisory as zero-order holds, considering a range of hold durations from 0.1 to 40 seconds. This method aims to provide timely and effective driving advisories while accommodating the dynamic nature of urban traffic environments.
Challenges of Deep Reinforcement Learning
Despite the evident similarities in higher frequency tasks associated with CAVs, a direct application of deep RL has proven inadequate for generalizing advisory autonomy tasks. The inability of deep RL to adapt to various traffic scenarios necessitates a new framework that can enhance performance consistency across diverse environments.
Introducing Temporal Transfer Learning (TTL)
To address the challenges posed by traditional methods, we introduce Temporal Transfer Learning (TTL) algorithms. These algorithms facilitate zero-shot transfer by training policies on a set of source tasks—specific traffic scenarios with designated hold durations. The efficacy of these policies is then evaluated on different target tasks.
Methodology
TTL systematically leverages the temporal structure of traffic scenarios to select the most suitable source tasks. The selection process is designed to maximize performance across the full range of advisory autonomy tasks. This adaptive mechanism is crucial for enhancing the performance of traffic optimization strategies.
Results
We validated our TTL algorithms across diverse mixed-traffic scenarios, measuring the performance against established baselines. The results demonstrate that TTL significantly outperforms traditional methods in reliably solving traffic optimization tasks.
Conclusion
This paper underscores the potential of coarse-grained advisory autonomy coupled with Temporal Transfer Learning in optimizing traffic flow. Our findings highlight a promising direction for future research, aiming to enhance the integration of CAV technologies into urban environments, ultimately contributing to improved traffic management and increased safety on the roads.
Future Work
- Exploring additional traffic scenarios to further validate TTL efficacy.
- Investigating the integration of machine learning with other traffic management systems.
- Assessing the long-term impacts of advisory autonomy on overall traffic efficiency.