Implementing Braking and Acceleration Features for a Car-Following Intelligent Vehicle
Document Type
Conference Proceeding
Publication Date
1-1-2023
Abstract
In this study, we have implemented braking and acceleration features for driver assistance in a car-following intelligent vehicle to minimize unnecessary braking and provide appropriate acceleration inputs in order to alleviate traffic buildups. Our system employs a fuzzy logic controller to process speed, obtained from an installed on-board diagnostics adapter, and distance, based from LIDAR sensors, inputs to output the necessary amount of braking or acceleration input to achieve the desired car-following targets while maintaining comfortable driving condition. Classifying driving behavior has been incorporated in the type of membership functions used to characterize the speed and distance inputs. Our hardware is easily installed in vehicles and does not require major changes to the vehicle. Finally, our experiments provide a 90% accuracy in achieving desired speed and maintaining inter-vehicular separation.
Recommended Citation
Marasigan, J.C.R., Mayuga, G.P.T., Magsino, E.R., Arada, G.P. (2023). Implementing Braking and Acceleration Features for a Car-Following Intelligent Vehicle. In: Ranganathan, G., Bestak, R., Fernando, X. (eds) Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, vol 475. Springer, Singapore. https://doi.org/10.1007/978-981-19-2840-6_20
