Abstract One of the greatest challenges that the cities of the world are facing today is traffic congestion. Driving a vehicle in highly concentrated traffic is not only challenging but also creates strain on the driver’s leg. Driver-assistance systems such as low speed adaptive cruise controller, stop and go controllers and Full-range adaptive cruise controller were designed to support the driver under highly concentrated traffic condition. Depending on the traffic density, the driving pattern changes from one city to another. Driving patterns are to be used in designing and optimizing such driver-assistant systems so as to obtain a driver-assistance controller with better safety and comfort. This paper proposes a fuzzy logic-based adaptive cruise control (FL-ACC) technique that has the ability to operate in a wide range of speeds including stop and go. The controller proposed here is designed and optimized for urban (stop and go) and highway scenarios. As the strength of the controller depends on the quality of the rule base used, soft computing techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) algorithms were used for optimizing the rule base of the fuzzy controller. The controller designed is tested for various driving cycles to validate the performance of the vehicle with the proposed controller.


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    Title :

    Optimized Fuzzy Logic-Based Adaptive Cruise Control Vehicle for Urban and Highway Driving Patterns


    Contributors:


    Publication date :

    2016-01-01


    Size :

    13 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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