Smart Driving of a Vehicle Using Model Predictive Control for Improving Traffic Flow

• Published in: IEEE transactions on intelligent transportation systems Vol. 15; no. 2; pp. 878 - 888
• Main Authors: Samad Kamal, Md Abdus, Imura, Jun-ichi, Hayakawa, Tomohisa, Ohata, Akira, Aihara, Kazuyuki
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-04-2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Automotive control; Congestion; connected vehicles environment; Delays; Density; Driving conditions; Jamming; Mathematical models; model predictive control (MPC); Predictive control; Roads; Traffic congestion; Traffic control; Traffic engineering; Traffic flow; Trajectory; Vehicle dynamics; Vehicles; Automotive control; connected vehicles environment; traffic congestion; model predictive control (MPC)
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2013.2292500

Traffic management on road networks is an emerging research field in control engineering due to the strong demand to alleviate traffic congestion in urban areas. Interaction among vehicles frequently causes congestion as well as bottlenecks in road capacity. In dense traffic, waves of traffic density propagate backward as drivers try to keep safe distances through frequent acceleration and deceleration. This paper presents a vehicle driving system in a model predictive control framework that effectively improves traffic flow. The vehicle driving system regulates safe intervehicle distance under the bounded driving torque condition by predicting the preceding traffic. It also focuses on alleviating the effect of braking on the vehicles that follow, which helps jamming waves attenuate to in the traffic. The proposed vehicle driving system has been evaluated through numerical simulation in dense traffic.