Coordinated Control Algorithm at Non-Recurrent Freeway Bottlenecks for Intelligent and Connected Vehicles

Abstract

Intelligent and Connected Vehicle (ICV) technology is considered to be a solution to improve the traffic performance. Through the information exchange in real-time among the vehicles, the roadside infrastructures, and the cloud platform, the sensing of the vehicles can be enhanced. This also enables coordinated driving decisions, which can improve traffic operations, especially at bottleneck locations. This paper addresses the problem of coordinating the vehicles near the bottleneck locations to help the vehicles passing the area quickly and smoothly. A lane advisory algorithm is designed to reduce conflicts by encouraging early lane changes. A coordinated vehicle movement planning algorithm is proposed to achieve a smooth longitudinal reference speed profiles for vehicles in the subject area. The algorithm can open enough headway for vehicles to change the lane and continue their trips. The effectiveness of the algorithm is evaluated using SUMO (Simulation of Urban MObility) as the simulation tool with no communication between vehicles as the benchmark case as well as the case where the vehicular traffic follows the so-called First-in-First-Out (FIFO) principle. The results of the evaluation summarize and indicate that the Coordinated Control Algorithm (CCA) proposed in this paper can improve traffic performance in terms of the average speed, the waiting time, the total travel time, and the traffic flow rate under different levels of service. INDEX TERMS Coordinated control, coordinated movement planning, intelligent and connected vehicles, lane advisory, non-recurrent bottlenecks.

V2X-Communication-Aided Autonomous Driving: System Design and Experimental Validation

“…Geometrically, each path candidate p candidate is generated by connecting the current position (x 0 , y 0 ) and sampled endpoint (x f , y f ), which is determined with a different lateral offset ∆W road (here, we set it to 0.5 m) based on a referential path. To take into account velocity and acceleration limits, each path candidate can be represented with a start state of the vehicle X 0 = [x 0 ,ẋ 0 ,ẍ 0 , y 0 ,ẏ 0 ,ÿ 0 ] T and the state of the endpoint with desired velocity and acceleration X f = [x f ,ẋ f ,ẍ f , y f ,ẏ f ,ÿ f ] T with time interval T := t f − t 0 as follows: 5 (2)…”

“…V2X also enables vehicles to obtain comprehensive information, which the sensor alone cannot record (e.g., traffic status, detailed states of neighboring vehicles, and information regarding construction sites that lay ahead). Based on the aforementioned characteristics, V2X shows significant potential for ensuring road safety, fuel efficiency, and manageable traffic flows [2][3][4][5]. Cooperative adaptive cruise control (CACC) or platooning by grouping two or more consecutive automated vehicles traveling along with a lead vehicle is one of the most well-known applications where V2X has been used for intelligent transportation.…”

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

“…Currently there are traffic light proposals that give priority to rescue vehicles [29,70]. However, the majority of them uses sensors as well as image detection and classification algorithms with low accuracy [17,29,70].…”

“…Additionally, the proposed solution reduces up to 50% of the waiting time for priority vehicles, compared to the FIFO strategy, which is still used in many related works [29]. This waiting time reduction is very important for emergency events.…”

Analysis of performance-based issues in green transportation management systems in smart cities

“…IoAV provides reliable, safe, seamless, and scalable communication between vehicles and on the road and offers a description of the autonomous vehicle communication system's configuration, properties, and safety risks. D Xiaoping et al introduced the intelligent and connected vehicle (ICV) (Xiaoping et al, 2020) system as a traffic management solution. The sensing of vehicles can be improved by the sharing of information in real-time with cars, the roadside facilities, and the cloud network.…”