An Empirical Evaluation of Deep Learning on Highway Driving (1504.01716v3)

Abstract: 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.

• The paper demonstrates that adapting Overfeat CNNs significantly improves real-time vehicle and lane detection in highway scenarios.
• The study introduces refined feature extraction and label handling techniques to achieve processing speeds up to 44Hz on high-end GPUs.
• The research validates the approach with a comprehensive dataset, underscoring its potential impact on advancing autonomous highway driving.

An Empirical Evaluation of Deep Learning on Highway Driving

This paper provides an empirical evaluation of deep learning techniques applied to highway driving scenarios, specifically focusing on computer vision tasks such as vehicle and lane detection using convolutional neural networks (CNNs). The research addresses the growing interest in self-driving technologies, particularly in controlled environments like highways where the driving conditions are more predictable compared to urban streets.

Methodology and Implementation

The paper leverages CNNs for real-time vehicle and lane detection with a focus on high efficiency and accuracy. By utilizing existing models like Overfeat, the authors adapt CNN architectures to process images at resolutions necessary for detecting vehicles at distances over 100 meters, achieving processing speeds greater than 10Hz on a standard laptop GPU.

Key innovations involve modifications to the Overfeat model, particularly in feature extraction and label handling. The authors alter the detector to activate within a stricter pixel region, thereby reducing ambiguity in bounding box predictions and enhancing the proposal accuracy. This method minimizes unnecessary bounding box predictions and ensures robust performance at high frame rates.

Experimental Setup and Data Collection

The research utilizes a 2014 Infiniti Q50 equipped with a variety of sensors, including camera systems, LIDAR, radar, and GPS. Data collection encompasses 14 days in diverse driving conditions, yielding a comprehensive dataset of over 17,000 image frames with vehicle annotations and 616,000 frames of lane annotations. The annotation process employs both automated techniques and human verification for accuracy.

Results and Discussion

The empirical results demonstrate that the adapted CNN model performs effectively in detecting vehicles and lane boundaries in real-time. The system achieves a processing speed of 44Hz using a GTX 780 Ti, with an expectation of 5Hz on the Nvidia PX1 chipset. Lane detection accuracy is robust up to 50 meters, with precision drop-offs at greater distances due to image resolution limitations.

In terms of vehicle detection, the paper emphasizes the comparative advantage of CNNs over traditional radar-based systems. While radar demonstrated nearly perfect precision, the CNN-based detector provided a more comprehensive detection framework with valid performance across various depths.

Implications and Future Work

This research underscores the feasibility of deploying CNNs for autonomous driving applications on highways, providing a framework that balances cost, speed, and accuracy. The paper suggests that future developments could focus on integrating temporal information for enhanced detection capabilities.

The paper's findings are significant for further advancements in autonomous vehicle technology, contributing to the practical application of deep learning models in real-world scenarios. Continued exploration in this field could potentially expand the capabilities of CNNs in more complex driving conditions, thus advancing the overall progress toward fully autonomous driving systems.