Harmonizing Multi-lane Traffic Flows Using Low-penetrated Cooperative Intelligent Vehicles

This paper presents a cooperative intelligent driving (CID) scheme to optimally control a vehicle’s speed in multi-lane traffic, smooth its flow, and facilitate others to improve their performance. Under the scheme, lane-wise traffic speeds along the road, in the form of a road-speed profile (RSP), are dynamically estimated using information from connected vehicles (CVs) that broadcast their states. The driving decision under the scheme is computed in a model predictive control (MPC) framework that optimizes the vehicle’s acceleration to equalize traffic speeds across the lanes in a cooperative approach besides attaining the objective of safe and smooth driving. The optimization problem in the scheme is solved using a real-time computation method. The scheme is assessed by implementing it on a small portion of vehicles in typical freeway traffic affected by lane blocks or merging flows using the AIMSUN traffic simulator. It is found that low penetration of CID can relieve bottlenecks, harmonize the flow over lanes, and significantly improve overall traffic performance.