Wide speed-range following control for road autonomous vehicles

Liu, Zilong; Chen, Wenbo; Jiang, Yanxia

doi:10.1109/cecnet.2011.5768824

Cited by 2 publications

(1 citation statement)

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“…5 Furthermore, intelligent systems are designed on the basis of the extent of the human driver's participation in driving; this means that an intelligent system is the driver's adviser, 6 is semiautonomous 7 or is autonomous. 8 Fully autonomous systems provide great comfort, safety and efficiency but reduce the driver's freedom in driving. In addition, various limitations in the real world make it necessary to design more practical systems based on the available facilities.…”

Section: Introductionmentioning

confidence: 99%

Optimal fuzzy control system design for car-following behaviour based on the driver–vehicle unit online delays in a real traffic flow

Ghaffari

Khodayari

Faraji

2014

Proceedings of the Institution of Mechanical Engineers, Part D:

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Promoting safety and comfort in driving and reducing the traffic, the pollution and the energy consumption are the main purposes of using many control systems in different driving processes. Car following is the dominant and effective behaviour in traffic flow, the automatization of which is crucial to achieving the aforementioned goals. In this paper, a novel optimal fuzzy control system is designed so that the follower vehicle maintains a safe distance from the vehicle in front of it in a traffic queue with a reduction in the energy consumption. This controller is superior in that it considers the online delays in the reaction of the driver–vehicle unit in the design. The instantaneous delay is estimated using the stimulus–reaction idea based on a real car-following data set. Tuning the controller is achieved by using the linear quadratic regulator gains in the fuzzy scaling gains. Considering the reaction delay enables the controller to be used in an advanced driver assistance system to obtain simultaneously a higher degree of safety, greater energy saving and more freedom for the driver. Fewer errors and more optimality in the results demonstrated the better performance of the proposed control system in comparison with those of a real driver and other controllers.

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Section: Introductionmentioning

confidence: 99%