Smart Cities Center

Learning Energy-Efficient Driving Behaviors by Imitating Experts

Kreidieh, Abdul Rahman
Fu, Zhe
Bayen, Alexandre M.
2022

The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are expected to serve as congestion-dampeners, mitigating the presence of instabilities that arise from various sources. However, in many applications, such maneuvers rely heavily on non-local sensing or coordination by interacting AVs, thereby rendering their adaptation to real-world settings a particularly difficult challenge. To address this challenge, this paper examines the role of imitation learning in...

New Aggregation Strategies to Improve Velocity Estimation from Single Loop Detectors

Coifman, Benjamin
Lee, Zu-Hsu
2000

Loop detectors are the preeminent vehicle detector for freeway traffic surveillance. Although single loops have been used for decades, debate continues on how to interpret the measurements. Many researchers have sought better estimates of velocity from single loops. The preceding work has emphasized post-processing techniques. Although rarely noted, these techniques effectively seek to reduce the bias due to long vehicles in measured occupancy and flow. This paper presents a different approach, using a new aggregation methodology to estimate velocity and reduce the impact of long vehicles...

Caltrans TOPS Evaluation: Assessing the Net Benefits of ITS Applications

Gillen, David
2001

This report describes the outcome of a set of experiments undertaken to assess the net benefits of ITS applications in a stylized urban and near urban highway network.