2025/04/26 by Hidayet Ersin Dursun, Dursun, Hidayet Ersin, Yusuf Güven +3
Engineering · #Advanced driver assistance systems #Aerospace and Aviation Technology #Artificial Intelligence (cs.AI) #Artificial neural network #Autonomous Vehicle Technology and Safety #Deep learning #FOS: Computer and information sciences #Imitation #Key (lock) #Robotics (cs.RO) #Sampling (signal processing) #Trajectory #Vehicle Dynamics and Control Systems #Work (physics)
paper · pdf · doi:10.48550/arxiv.2504.18847
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This work focuses on the design of a deep learning-based autonomous driving system deployed and tested on the real-world MIT Racecar to assess its effectiveness in driving scenarios. The Deep Neural Network (DNN) translates raw image inputs into real-time steering commands in an end-to-end learning fashion, following the imitation learning framework. The key design challenge is to ensure that DNN predictions are accurate and fast enough, at a high sampling frequency, and result in smooth vehicle operation under different operating conditions. In this study, we design and compare various DNNs, to identify the most effective approach for real-time autonomous driving. In designing the DNNs, we adopted an incremental design approach that involved enhancing the model capacity and dataset to address the challenges of real-world driving scenarios. We designed a PD system, CNN, CNN-LSTM, and CNN-NODE, and evaluated their performance on the real-world MIT Racecar. While the PD system handled basic lane following, it struggled with sharp turns and lighting variations. The CNN improved steering but lacked temporal awareness, which the CNN-LSTM addressed as it resulted in smooth driving performance. The CNN-NODE performed similarly to the CNN-LSTM in handling driving dynamics, yet with slightly better driving performance. The findings of this research highlight the importance of iterative design processes in developing robust DNNs for autonomous driving applications. The experimental video is available at https://www.youtube.com/watch?v=FNNYgU--iaY.