Autonomous Vehicles

Computationally Aware Cyber-Physical Systems

Predicting how a system will behave in the future requires more computing power if that system is complex. Navigating through environments with many obstacles could require significant computing time, which may delay the issue of decisions that have to be made by the on-board algorithms. This project seeks to develop new theory for controller design which incorporates selecting the best model to use when making a decision in real time.

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CAN Coach: Vehicular Control through Human Cyber-Physical Systems

M. Nice, Elmadani, S., Bhadani, R., Bunting, M., Sprinkle, J., and Work, D., “CAN Coach: Vehicular Control through Human Cyber-Physical Systems”, in Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems, New York, NY, USA, 2021.

Real-Time Traffic Lights Identification using YOLOv3 Algorithm For Autonomous Vehicles

R. Kozel and Robert, N., “Real-Time Traffic Lights Identification using YOLOv3 Algorithm For Autonomous Vehicles”, in CAT Vehicle 2020 Technical Reports, 2020. Document: Download preprint (2.22 MB)

Hyperparameter Optimization using Grid Search for use in Monocular Depth Estimation

C. Barrett and Bessho, T., “Hyperparameter Optimization using Grid Search for use in Monocular Depth Estimation”, in CAT Vehicle 2020 Technical Reports, 2020. Document: Download preprint (428.32 KB)

Modeling Human Car-Following Behavior from Demonstration with Recurrent Neural Networks

I. Jones and Walter, M., “Modeling Human Car-Following Behavior from Demonstration with Recurrent Neural Networks”, 2020. Document: Download preprint (959.97 KB)

Real-Time Traffic Lights Identification using YOLOv3 Algorithm For Autonomous Vehicles

Authors

Rachel Kozel

Naeemah Robert

Date of publication:

2020

Research Categories

Autonomous Vehicles

Traffic

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Safer Adaptive Cruise Control for Traffic Wave Dampening

E. Baschab, Ball, S., Vazzana, A., and Sprinkle, J., “Safer Adaptive Cruise Control for Traffic Wave Dampening”, in ACM/IEEE 12th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2021), 2021.

CIRCLES

The Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) project aims to reduce instabilities in traffic flow, called "phantom jams," that cause congestion and wasted energy. If you have ever encountered a temporary traffic jam for no apparent reason, this might have been a phantom jam that occurred naturally because of human driving behavior.