Neural network is one of the most widely used method in autonomous driving. Current researchers use only steering angle to train artificial neural networks, ignoring the importance of acceleration and deceleration for autonomous vehicles. We used an intelligent driving platform built with the Raspberry Pi 4 Model B, a front wide-angle camera, and a 1:16 scale model car to achieve real-time acceleration and deceleration while performing road tracking. Existing models cannot learn steering angle and throttle values well. This research proposed a novel architecture CNN model (PBLM-CNN21) to achieve real-time acceleration and deceleration while achieving road tracking. The PBLM-CNN21 model can learn steering angle and throttle value. The training loss value of our proposed PBLM-CNN21 model was 35% lower than the current TDD model, and the stability of our proposed road tracking model was 82% greater than that of the current TDD model. Furthermore, we tested the impact of different hyper-parameters on training model loss and road tracking performance. In addition, we also tested the effectiveness of varying lighting conditions and speed ratios on road tracking performance. The PBLM-CNN21 model proved more robust than the existing TDD models. Moreover, the PBLM-CNN21 model achieved road tracking under different lighting conditions and was more suitable for high-speed ratios.
Keywords: autonomous driving; raspberry pi; deep learning; road tracking; convolutional neural networks; PBLM-CNN21
*Corresponding author: Tel.: (+66) 0966033973
E-mail: jianqu@pim.ac.th
