| 1. |
- Faris, Muhammad, 1991, et al.
(författare)
-
A Sensitivity-based Heuristic for Vehicle Priority Assignment at Intersections
- 2023
-
Ingår i: IFAC-PapersOnLine. - 2405-8963. ; 56:2, s. 4922-4928
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a sensitivity-based heuristic to address the dynamic priority assignment problem of connected and autonomous vehicle (CAV) and human-driven vehicle (HDV) at traffic intersections. We exploit sensitivity analysis tools to approximatively predict the CAV's performance violation as a function of the HDV states. Such predictions are then used to decide on a crossing order that preserves optimality and feasibility despite the behavior of the HDV. The proposed algorithm is compared with the baseline first-come, first-serve (FCFS) and mixed-integer nonlinear programming (MINLP) approaches. In the closed-loop simulation, we show that the heuristic is computationally much faster than MINLP and able to retain a close-to-optimal solution, which is far better than FCFS.
|
|
| 2. |
- Faris, Muhammad, 1991, et al.
(författare)
-
Optimization-Based Coordination of Mixed Traffic at Unsignalized Intersections Based on Platooning Strategy
- 2022
-
Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. ; 2022-June, s. 977-983
-
Konferensbidrag (refereegranskat)abstract
- This paper considers a coordination problem for Connected and Automated Vehicles (CAVs) in mixed traffic at unsignalized intersections. In such a setting, the behavior of the Human-Driven Vehicles (HDVs) is difficult to predict, thus challenging the formulation and the solution of the coordination problem. To solve this problem, we propose a coordination strategy, where CAVs are used as both sensors and actuators in mixed platoons. A timeslot-based approach is used to coordinate the occupancy of the intersection and to compensate for the HDVs behavior. The proposed approach has a bi-level optimization structure built upon the Model Predictive Control (MPC) framework that decides the crossing order and computes the vehicles' commands. In simulations, we show that the choice of the HDV prediction model heavily affects the coordination by evaluating the performance of two different HDV models: car-following and constant velocity, where the latter demonstrates more consistent results in the presence of deviation of the HDVs' behavior from a nominal model.
|
|
