Search Results for author: Jorge Laval

Found 5 papers, 1 papers with code

Impact of Acceleration/deceleration Limits on the String Stability of Adaptive Cruise Control

no code implementations9 Aug 2021 Hao Zhou, Anye Zhou, Tienan Li, Danjue Chen, Srinivas Peeta, Jorge Laval

This paper demonstrates that the acceleration/deceleration limits in ACC systems can make a string stable ACC amplify the speed perturbation in natural driving.

Fundamental Diagrams of Commercial Adaptive Cruise Control: Worldwide Experimental Evidence

no code implementations12 May 2021 Tienan Li, Danjue Chen, Hao Zhou, Yuanchang Xie, Jorge Laval

Experimental measurements on commercial adaptive cruise control (ACC) vehicles \RoundTwo{are} becoming increasingly available from around the world, providing an unprecedented opportunity to study the traffic flow characteristics that arise from this technology.

Significance of Low-level Controller for String Stability under Adaptive Cruise Control

no code implementations15 Apr 2021 Hao Zhou, Anye Zhou, Tienan Li, Danjue Chen, Srinivas Peeta, Jorge Laval

Current commercial adaptive cruise control (ACC) systems consist of an upper-level planner controller that decides the optimal trajectory that should be followed, and a low-level controller in charge of sending the gas/brake signals to the mechanical system to actually move the vehicle.

Congested Urban Networks Tend to Be Insensitive to Signal Settings: Implications for Learning-Based Control

2 code implementations21 Aug 2020 Jorge Laval, Hao Zhou

Notably, we found that no control (i. e. random policy) can be an effective control strategy for a surprisingly large family of networks.

Review of Learning-based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps between Self-driving and Traffic Congestion

no code implementations2 Oct 2019 Hao Zhou, Jorge Laval, Anye Zhou, Yu Wang, Wenchao Wu, Zhu Qing, Srinivas Peeta

Some suggestions towards congestion mitigation for future mMP studies are proposed: i) enrich data collection to facilitate the congestion learning, ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and iii) integrate domain knowledge from the traditional car following (CF) theory to improve the string stability of mMP.

Autonomous Vehicles BIG-bench Machine Learning +3

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