LaneNet: Real-Time Lane Detection Networks for Autonomous Driving (1807.01726v1)

Abstract: Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane. It severs as one of the key techniques to enable modern assisted and autonomous driving systems. However, several unique properties of lanes challenge the detection methods. The lack of distinctive features makes lane detection algorithms tend to be confused by other objects with similar local appearance. Moreover, the inconsistent number of lanes on a road as well as diverse lane line patterns, e.g. solid, broken, single, double, merging, and splitting lines further hamper the performance. In this paper, we propose a deep neural network based method, named LaneNet, to break down the lane detection into two stages: lane edge proposal and lane line localization. Stage one uses a lane edge proposal network for pixel-wise lane edge classification, and the lane line localization network in stage two then detects lane lines based on lane edge proposals. Please note that the goal of our LaneNet is built to detect lane line only, which introduces more difficulties on suppressing the false detections on the similar lane marks on the road like arrows and characters. Despite all the difficulties, our lane detection is shown to be robust to both highway and urban road scenarios method without relying on any assumptions on the lane number or the lane line patterns. The high running speed and low computational cost endow our LaneNet the capability of being deployed on vehicle-based systems. Experiments validate that our LaneNet consistently delivers outstanding performances on real world traffic scenarios.

LaneNet: A Two-Stage Approach for Real-Time Lane Detection in Autonomous Driving

The paper "LaneNet: Real-Time Lane Detection Networks for Autonomous Driving" presents a novel method for the detection of lane lines, aimed at improving the robustness and efficiency of autonomous driving systems. The proposed LaneNet resolves the intricacies involved in lane detection through a deep neural network-based pipeline, systematically partitioning the task into two distinct stages: lane edge proposal and lane line localization.

Problem Statement and Challenges

Lane detection is essential for the effective functioning of advanced driver assistance systems (ADAS) and autonomous vehicles. However, it poses several challenges due to the simplistic appearance of lanes, which might be confused with other elements in the driving environment such as road marks, reflections, or guardrails. The variability in lane patterns further complicates detection efforts. Prior approaches often depended on restrictive assumptions about lane geometry, which are not universally applicable, particularly in more complex urban driving scenarios.

Methodology: LaneNet Architecture

LaneNet introduces an innovative two-stage process to enhance lane detection efficiency and accuracy:

1. Lane Edge Proposal Stage: This stage employs a convolutional neural network (CNN) to perform pixel-wise classification of lane edges in images using a light-weight encoder-decoder architecture. Depthwise separable convolutions and 1x1 convolutions are employed to maintain a low computational cost and high processing speed. The output is a binary lane edge proposal map demonstrating high robustness against false positives.
2. Lane Line Localization Stage: Utilizing the edge proposals, a second network localizes lane lines by predicting their geometric parameters. Input features from the previous stage are encoded into a holistic representation, and a Long Short-Term Memory (LSTM) network is utilized to iteratively decode the lane line parameters. This approach accommodates various lane configurations, providing reliable lane detection performance across different environments.

Experimental Results

The LaneNet is validated across over 5,000 real-world traffic images covering both highways and urban settings. Metrics for assessing true positive rates (TPR) and false positive rates (FPR) are utilized. LaneNet achieves a TPR of 97.9% and an FPR of 2.7% in simpler environments, with similarly high performance in more challenging scenarios (TPR of 96.7% and FPR of 3.9%). These results significantly outperform comparison methods, demonstrating LaneNet’s capacity to effectively handle a wide array of complex scenarios.

Computational Efficiency

An emphasis on computational cost is evident in LaneNet’s design, which enables deployment on vehicle platforms equipped with limited computational resources. Tested on GPUs such as NVIDIA Titan Xp and Jetson TX1, LaneNet achieves high frame rates, making it suitable for real-time lane detection. The model maintains a compact architecture with a size under 1GB, making integration with vehicle systems feasible.

Implications and Future Directions

LaneNet's advancement introduces a robust method for lane detection void of stringent assumptions on lane geometry. This is particularly beneficial for the deployment of ADAS in diverse driving environments. Moreover, the innovative weakly-supervised training strategy for the localization network reduces the dependency on heavily annotated datasets, thereby expediting the development cycle.

Looking forward, further research could focus on integrating lane tracking into the LaneNet framework to stabilize performance over sequences of frames, enhancing continuity and reliability in lane detection. Additionally, exploring the integration of other sensory data, such as LIDAR or radar, with the optical detection method presented here could further enhance system robustness. The continued refinement and validation of LaneNet promises a tangible impact on the development and deployment of safer, more reliable autonomous driving systems.