Cooperative automated driving strategies for efficient traffic operations near on-ramp bottlenecks

Subject

Traffic congestion is a societal problem worldwide. Bottlenecks are common places where traffic congestion starts. Highway on-ramps are typical bottlenecks. The merging demand of the on-ramp vehicles is the main reason to trigger traffic congestion. Connected Automated Vehicles (CAVs), equipped with Vehicle to  Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications, have potentials to alleviate traffic congestion. This benefits can be achieved by designing driving strategies for CAVs properly. The main objectives of this project are to design efficient and safe cooperative driving strategies for CAVs to facilitate on-ramp merging process.

Scientific challenges

The merging process includes longitudinal and lateral motions. One of the scientific challenges is to ensure safe lane changing process when CAVs are involved. To alleviate on-ramp traffic congestion, the merging capacity is to expand. Another scientific challenge is to improve the efficiency of the merging process. The most difficult scientific challenge is how to improve traffic performances with low CAVs market penetration.

Societal relevance

Highway on-ramps are important connection facilities. The traffic capacity of them affects travel time, fuel consumption, economic development and air pollution. This study focuses on improving on-ramp traffic operations with the upcoming CAVs. The longitudinal and lateral driving strategies for CAVs will be designed to achieve efficient and safe on-ramp merging process. This will give theoretical basis for the future CAVs production.

Na Chen

Start/end date: 6 September, 2016 - 6 September, 2020
Promotors: Bart van Arem and Meng Wang

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