From Individual Automated Vehicles to Cooperative Traffic Management (IAVTRM)

Background

Automated driving has great potential to improve road safety, traffic flow efficiency and fuel economy and automation technology has been successfully demonstrated on public roads. However, before automation can be introduced in consumer cars, benefits and risks shall be evaluated in complex real world driving conditions with real human ‘drivers’ interacting with other road users. The IAVTRM project’s aim is to investigate highly automated driving with 1) individual automated vehicles, 2) cooperative platoons, and 3) cooperative traffic management through infrastructure communication.

Goal of the project

  1. To design new algorithms for automated driving of individual vehicles and their safe platooning will be designed.
  2. To develop innovative human-machine interfaces, enabling safe transitions between manual and automated driving.
  3. To investigate the human interaction with automation in driving simulators, on a test track, on a closed highway and in mixed traffic on public highways.
  4. To examine the influence of individual and cooperative automation with increasing penetration levels on road safety, traffic flow efficiency, road capacity and fuel economy.

Work programme

The research in DCSC is related to the following work packages of IAVTRM project:

  • The development of algorithms for automated path-following, lane keeping and changing, merging and overtaking with cooperative adaptive cruise control (CACC).
  • Algorithms for safe and fault-tolerant vehicle platooning and analysis of their influence on the traffic flow and fuel efficiency.
  • Mathematical analysis of microscopic traffic flow models, describing interactions of automated vehicles, human drivers and other road users.

Project team members

Other team members from TU Delft:

Keywords

Automated driving, intelligent vehicle, cooperative adaptive cruise control (CACC), platooning, traffic flow modeling.

Sponsored by

NWO Domain TTW

Partners

Toyota Motor Europe, TNO, NXP, Imtech, RDW, Connekt , SWOV, Imtech, Technolution , Almende, Trinité Automation, VisLab

Outcome

Algorithms and software for automated driving will be developed  that are compliant with the safety and comfort requirements and can be verified by formal methods. These algorithms will be tested on the real-time vehicle simulators and, later, on real vehicles operating on highways and in urban areas. The influence of vehicle automation on the road traffic and fuel efficiency will be studied.

Selected publications

  1. Polarization in coopetitive networks of heterogeneous nonlinear agents, Proskurnikov AV, Cao M, Proceedings of IEEE Conference on Decision and Control 2016, December 11-14, Las Vegas, pp. 6915-692
  2. Simple synchronization protocols for heterogeneous networks: beyond passivity, Proskurnikov AV, Mazo M. Jr., Proceedings of IFAC World Congress 2017, July 9-13, Toulouse, France, pp. 9836—9841
  3. A Guiding Vector-Field Algorithm for Path-Following Control of Nonholonomic Mobile Robots, Y. Kapitanyuk, A.V. Proskurnikov, M. Cao,  IEEE Transactions on Control Systems Technology, 2018 (published online, DOI 10.1109/TCST.2017.2705059)