Heterogeneous Traffic Management using METANET Model with Filtered Feedback Linearization Control Approach
2022-01-0092
03/29/2022
- Event
- Content
- This paper presents a Filtered-Feedback-Linearization (FFL) controller for a non-signalized heterogeneous traffic network consisting of human-driven and autonomous vehicles at a macroscopic level. The FFL controller requires limited model information and it is effective for command following and rejection of unknown-and-unmeasured disturbances. This paper distinguishes between human-driven and autonomous vehicles in terms of their operational characteristics and controllability. To describe the traffic network's behavior, we introduce an extended heterogeneous METANET model wherein the traffic flow, density, and velocity dynamics of each vehicle class are described. To develop traffic control policies, we propose a filtered-feedback-linearization control approach wherein the autonomous vehicles are modeled as controllable agents and human-driven vehicles are modeled as non-controllable but rational agents. FFL controller sends the optimal suggested velocity of autonomous vehicles as the controller command to the traffic system in order to reach the desired average flow rate of each sub-network. Numerical simulation demonstrates the effectiveness of the proposed approach in managing the traffic flow of a heterogeneous traffic system.
- Citation
- Karimi Shahri, P., and Ghasemi, A., "Heterogeneous Traffic Management using METANET Model with Filtered Feedback Linearization Control Approach ," SAE Technical Paper 2022-01-0092, 2022, .