# An Empirical Evaluation of Deep Learning on Highway Driving

arXiv: https://arxiv.org/abs/1504.01716 · HTML: https://mlsanity.com/paper/1504.01716/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2015.

## Built on by (most cited first, 30 of 35)

- [DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving](https://mlsanity.com/paper/2401.03641/) (2024)
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- [Rethinking the Evaluation Protocol of Domain Generalization](https://mlsanity.com/paper/2305.15253/) (2023)
- [LaneCPP: Continuous 3D Lane Detection using Physical Priors](https://mlsanity.com/paper/2406.08381/) (2024)
- [End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving](https://mlsanity.com/paper/2102.04738/) (2021)
- [Distributionally Robust Learning with Stable Adversarial Training](https://mlsanity.com/paper/2106.15791/) (2021)
- [Relative stability toward diffeomorphisms indicates performance in deep nets](https://mlsanity.com/paper/2105.02468/) (2021)
- [Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control](https://mlsanity.com/paper/2101.05328/) (2021)
- [Instance-Aware Predictive Navigation in Multi-Agent Environments](https://mlsanity.com/paper/2101.05893/) (2021)
- [How deep convolutional neural networks lose spatial information with training](https://mlsanity.com/paper/2210.01506/) (2022)
- [Visual Explanations with Attributions and Counterfactuals on Time Series Classification](https://mlsanity.com/paper/2307.08494/) (2023)
- [SparseLaneSTP: Leveraging Spatio-Temporal Priors with Sparse Transformers for 3D Lane Detection](https://mlsanity.com/paper/2601.04968/) (2026)
- [GLane3D : Detecting Lanes with Graph of 3D Keypoints](https://mlsanity.com/paper/2503.23882/) (2025)
- [Temporal Feature Networks for CNN based Object Detection](https://mlsanity.com/paper/2103.12213/) (2021)
- [Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles](https://mlsanity.com/paper/2209.07042/) (2022)
- [End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving](https://mlsanity.com/paper/2404.10849/) (2024)
- [Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection](https://mlsanity.com/paper/2503.06237/) (2025)
- [Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer](https://mlsanity.com/paper/2112.06281/) (2021)
- [Multi-task UNet architecture for end-to-end autonomous driving](https://mlsanity.com/paper/2112.08967/) (2021)
- [Defending Adversarial Patches via Joint Region Localizing and Inpainting](https://mlsanity.com/paper/2307.14242/) (2023)
- [Savvy: Trustworthy Autonomous Vehicles Architecture](https://mlsanity.com/paper/2402.14580/) (2024)
- [ENet-21: An Optimized light CNN Structure for Lane Detection](https://mlsanity.com/paper/2403.19782/) (2024)
- [Computer vision-based model for detecting turning lane features on Florida's public roadways](https://mlsanity.com/paper/2406.08822/) (2024)
- [Work-Efficient Parallel Non-Maximum Suppression Kernels](https://mlsanity.com/paper/2502.00535/) (2025)
- [An Evolution of CNN Object Classifiers on Low-Resolution Images](https://mlsanity.com/paper/2101.00686/) (2021)
- [Defending Against Adversarial Denial-of-Service Data Poisoning Attacks](https://mlsanity.com/paper/2104.06744/) (2021)
- [WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving](https://mlsanity.com/paper/2108.12134/) (2021)

## Abstract (arXiv)

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.