no code implementations • 9 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.
no code implementations • 15 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.
no code implementations • 1 Feb 2021 • Chaojie Wang, Srinivas Peeta, Jian Wang
The first stage incorporates a decentralized local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles' knowledge of local traffic information.
Autonomous Vehicles Physics and Society Computer Science and Game Theory Systems and Control Systems and Control
no code implementations • 2 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.
no code implementations • 13 Dec 2017 • Lei Lin, Zhengbing He, Srinivas Peeta
Two architectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series.