Mixed Traffic Control Involving Manually-Controlled and Automatically-Controlled Vehicles in IVHS

  • Su-Nan Huang
  • Steven C. Chan
  • Wei Ren
Part of the International Series on Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 18)


Traffic congestion is a global problem. Intelligent Vehicle Highway Systems (IVHS) have been proposed to help solve this problem (see [1, 2, 3, 4, 5, 6, 7, 8, 9]). The works of [1, 2, 3] propose a fully automated traffic system in which vehicles are automated and organized in platoons through interactive vehicle-to-vehicle communication. This approach can yield superior control quality and has the best potential for throughput improvement but is difficult to implement due to high costs and heavy infrastructure requirements. The reports in [4, 5, 6, 7] suggest an autonomous intelligent cruise control for the IVHS, i.e., to keep a desired velocity when there is no vehicle in front, and to keep a desired spacing (possibly velocity dependent) when there is a vehicle in front. This approach allows a mixture of automated and non-automated vehicles and requires little vehicle-to-vehicle communication. However, it is not a completely self-contained intelligent controller that can handle all possible scenarios such as sudden lane changes, following a vehicle with rapid acceleration or deceleration, emergency braking, switching between human drivers and automatic controllers, etc. In [8, 9], artificial intelligence is used to design fully autonomous vehicle control system which can operate in mixed traffic. The research in this area is mostly concerned with handling complex traffic scenarios and do not attempt to optimize control performance for tight vehicle spacing and passenger comfort. In addition, the previous results do not provide the performance analysis of mixed traffic control, though they claim their control can be used in an environment of automatically controlled and manually controlled vehicles. It is therefore necessary to design a complete intelligent control system that 1) handles all possible scenarios in mixed traffic, 2) optimizes control performance so that tight vehicle spacing can be achieved with minimum passenger discomfort, 3) evaluates the performance of mixed traffic.


Lane Change Optimal Velocity Bernoulli Random Variable Mixed Traffic Human Driver 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Su-Nan Huang
    • 1
  • Steven C. Chan
    • 1
  • Wei Ren
    • 1
  1. 1.Dept of Electrical Engineering and Computer SciencesUniversity of California at BerkeleyBerkeleyUSA

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