# Autonomous Driving with a Deep Dual-Model Solution for Steering and Braking Control

arXiv: https://arxiv.org/abs/2405.06473 · HTML: https://mlsanity.com/paper/2405.06473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 6 of 6)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars](https://mlsanity.com/paper/1712.01397/) (2017)
- [DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems](https://mlsanity.com/paper/2208.11212/) (2022)
- [End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving](https://mlsanity.com/paper/2404.10849/) (2024)

## Abstract (arXiv)

The technology of autonomous driving is currently attracting a great deal of interest in both research and industry. In this paper, we present a deep learning dual-model solution that uses two deep neural networks for combined braking and steering in autonomous vehicles. Steering control is achieved by applying the NVIDIA's PilotNet model to predict the steering wheel angle, while braking control relies on the use of MobileNet SSD. Both models rely on a single front-facing camera for image input. The MobileNet SSD model is suitable for devices with constrained resources, whereas PilotNet struggles to operate efficiently on smaller devices with limited resources. To make it suitable for such devices, we modified the PilotNet model using our own original network design and reduced the number of model parameters and its memory footprint by approximately 60%. The inference latency has also been reduced, making the model more suitable to operate on resource-constrained devices. The modified PilotNet model achieves similar loss and accuracy compared to the original PilotNet model. When evaluated in a simulated environment, both autonomous driving systems, one using the modified PilotNet model and the other using the original PilotNet model for steering, show similar levels of autonomous driving performance.