A Framework for Lane-Change Maneuvers of Connected Autonomous Vehicles in a Mixed-Traffic Environment

Abstract

In the transition era towards connected autonomous vehicles (CAVs), the sharing of the roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected to pose safety and flow efficiency concerns even though CAVs may tend to adopt rather conservative maneuvering policies. Unfortunately, this will likely cause HDV drivers to unduly exploit such conservativeness by driving in ways that imperil safety. A context of this situation is lane-changing by the CAV, a potential major source of traffic disturbance at multi-lane highways that could impair their traffic flow efficiency. In dense, high-speed traffic conditions, it will be extremely unsafe for the CAV to change lanes without cooperation from neighboring vehicles in the traffic stream. To help address this issue, this paper developed a framework through which connected HDVs (CHDVs) could cooperate to facilitate safe and efficient lane-changing by the CAV. A numerical experiment was carried out to demonstrate the efficacy of the framework. The results indicated the CAVs’ lane-changing feasibility and the overall duration of the lane-changing if the CAV carries out that maneuver. It was observed that throughout the lane-changing process, the safety of not only the CAV but also of all neighboring vehicles, was promoted through the framework’s collision avoidance mechanism. The overall traffic flow efficiency was analyzed in terms of the ambient level of CHDV–CAV cooperation. Overall, the results of the study present evidence of how CHDV–CAV cooperation can help enhance the overall system efficiency.

A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure

“…Most known AV demonstrations have been carried out on simple roads that offer a conducive environment for AV operations, for example, well-marked lanes and high standards of road design [89]. Recent research studies have adopted assumptions regarding the consistency of lane/road texture, lane/road width, alignment, and surface markings [91,92]. However, these conditions do not exist on relatively less structured roads where there is greater uncertainty in the design and operational conditions of roadways.…”

Simulating and Modelling the Safety Impact of Connected and Autonomous Vehicles in Mixed Traffic: Platoon Size, Sensor Error, and Path Choice

“…These subsystems must be simulated to address the challenges posed by various road network layouts. For the longitudinal and lateral control of CAVs, numerous algorithms for path tracking [4] and lane-changing [5][6][7] have been developed. Additionally, extensive research has utilised traffic simulation to examine the traffic and safety impacts of longitudinal control algorithms and the formation of vehicle platoons on motorways and freeways [8][9][10][11].…”