Artificial intelligence (AI) is now growing with the utmost versatility. There are many unsolved problems resolved by introducing AI since earlier days in this present century. Many researchers have solved problems related to autonomous vehicle control by considering path planning and collision avoidance. These parameters are highly involved in the design of unmanned autonomous vehicles. It has been found that there are some concerned results that actually evolved with a good impact in the case of the simulation model. Meanwhile, during hardware application, it is quite difficult to implement the same due to errors from the sensor data and entire data processing. In this chapter, a novel cascaded fuzzy logic–based approach is discussed for path planning and collision avoidance control mechanisms for an autonomous vehicle. Simulation is performed in a MATLAB environment and validated by designing three separate fuzzy inference systems (FISs). One of the FISs collected data for collision avoidance by considering obstacles and their distance as inputs. The other FISs collected data from LiDAR sensors, and the output from collision avoidance FIS, to decide whether to stop, move, or turn as per the outcome achieved after the final defuzzification.


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    Title :

    Type 1 Cascaded Fuzzy Logic–Based Autonomous Vehicles Control Applications


    Contributors:


    Publication date :

    2025-06-24


    Size :

    21 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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