Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Zhang, Yuheng; Liu, Luning; Lu, Zhaoming; Wang, Luhan

doi:10.1109/access.2020.3002825

Cited by 13 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The vehicle can only select a certain speed to enter the intersection. The constraint is expressed in Equation (16).…”

Section: Grid Entering and Exiting Time Constraintmentioning

confidence: 99%

“…The mechanism uses priority setting, window searching and trajectory planning to ensure safety and efficiency. In [16], proposes a robust autonomous intersection control (AIC) approach with global optimization scheduling and demonstrates its superiority in transportation efficiency and robustness. In [17], devised a collaborative, sequence-based control system and put forward two control strategies to determine the safe passing sequence of vehicles at intersections.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Route Planning and Traffic Control Model for Autonomous Intersections by Minimizing Conflict Zones

Liu,

Long,

2023

IEEE Access

View full text Add to dashboard Cite

Most existing studies on autonomous intersection management (AIM) primarily focus on modeling and resolving conflicts between vehicles within an intersection, assuming predetermined routes of the autonomous vehicles (AVs) as exogenous inputs. Additionally, these studies presume scenarios in which AVs traverse the intersection at a constant speed without stopping. However, such scenarios are difficult to realize under heavy traffic demand. To address this issue, this study firstly discretized the intersection into numerous grids and proposed formulations to calculate the time at which the vehicles enter and exit a given grid at different speeds and accelerations based on the outer-boundary-projection dimension-reduction method. Thereafter, a bi-level programming model was established to optimize the route choices and traffic control schemes. The upper-level model aimed to minimize the conflicts within the intersection zones, considering the lane options for vehicles entering and exiting the intersection as the decision variable to optimize the AV routes. In addition, the lower-level model strived to minimize the delay for all upcoming vehicles. The time when a vehicle enters an intersection and whether it stops are utilized as decision variables. Based on the sliding time-window technique, the proposed model was transformed into a mixed-integer linear programming (MILP) problem, which is compiled by a mathematical programming language (AMPL) and solved by CPLEX. The numerical analysis shows that the proposed models significantly reduced the conflicts between the vehicles, and consequently, improved the space utilization of the intersection, reduced vehicle delays, and saved a significant amount of energy.

show abstract

“…The vehicle can only select a certain speed to enter the intersection. The constraint is expressed in Equation (16).…”

Section: Grid Entering and Exiting Time Constraintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Route Planning and Traffic Control Model for Autonomous Intersections by Minimizing Conflict Zones

Liu,

Long,

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Zhang et al. [14] proposed a robust control method based on global optimal scheduling, and the control performance of the algorithm with a packet loss rate of 0% to 20% was verified.…”

Section: Introductionmentioning

confidence: 99%

Improved model‐free adaptive predictive control‐based cooperative driving control for connected and automated vehicles subject to time‐varying communication delays and packet losses at signal‐free intersections

Jiang

Luo

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

In a vehicle‐to‐infrastructure (V2I) communication network at signal‐free intersections, time‐varying delays and random packet losses may occur because of the unreliable nature of wireless communication, which can degrade the performance of cooperative driving for large‐scale connected and automated vehicles (CAVs). Consequently, to solve this problem, this paper proposed a cooperative driving control method based on improved model‐free adaptive predictive control (IMFAPC). I/O data of the target CAVs during driving are adopted to realize the networked predictive control of the time‐varying transmission delays and packet losses in the dynamic linearization prediction scheme and moving horizon prediction control technology. And then the vehicle can be allocated the expected speed to realize accurate vehicle control. In addition, the convergence of the IMFAPC‐based cooperative driving control algorithm is proven by conducting a stability analysis. Extensive numerical experiments verified the effectiveness of the proposed control strategy, and the results demonstrate that the proposed method can alleviate the adverse effects of time‐varying delays and random packet losses on the control effects, thereby improving traffic efficiency under various traffic volumes on the basis of ensuring the safety of multi‐vehicle driving.

show abstract

Privacy-Preserving Dynamic Task Scheduling for Autonomous Vehicles

Muthurajkumar

Karthikeyan

Pradeep

et al. 2022

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Cited by 13 publications

References 37 publications

A Route Planning and Traffic Control Model for Autonomous Intersections by Minimizing Conflict Zones

A Route Planning and Traffic Control Model for Autonomous Intersections by Minimizing Conflict Zones

Improved model‐free adaptive predictive control‐based cooperative driving control for connected and automated vehicles subject to time‐varying communication delays and packet losses at signal‐free intersections

Privacy-Preserving Dynamic Task Scheduling for Autonomous Vehicles

Contact Info

Product

Resources

About