Towards End-to-End Lane Detection: an Instance Segmentation Approach (1802.05591v1)

Abstract: Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed "bird's-eye view" transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results.

• The paper proposes LaneNet, a novel multi-task architecture that uses instance segmentation to dynamically detect lanes without fixed class numbers.
• It introduces H-Net, a perspective transformation network that enhances lane fitting by adapting to varying road geometries.
• The approach achieved 96.4% accuracy on the tuSimple dataset and operates in real time at 50 fps, advancing autonomous vehicle safety.

End-to-End Lane Detection: An Instance Segmentation Approach

The paper, "Towards End-to-End Lane Detection: An Instance Segmentation Approach," presents a novel method for lane detection in autonomous vehicles, addressing limitations in previous methodologies. Traditional techniques often involve hand-crafted features and are computationally intensive, whereas recent deep learning approaches are constrained by their inability to handle variable lane numbers or lane changes. This paper introduces a fresh perspective by recasting lane detection as an instance segmentation problem.

Key Methodological Contributions

The authors propose LaneNet, a multi-task network that tackles lane detection using an instance segmentation paradigm. Unlike previous models that relied on fixed numbers of lane classes, LaneNet can dynamically handle a variable number of lanes and adapt to lane changes. This adaptability is achieved via two main branches:

1. Segmentation Branch: Trained to generate binary lane masks, it identifies pixels corresponding to lanes, differentiating them from the background.
2. Embedding Branch: Outputs embeddings for lane pixels, trained with a clustering loss that ensures pixels from the same lane have similar embeddings. This enables the network to cluster lane pixels into distinct instances, each representing a separate lane.

By splitting lane detection into these tasks, the network efficiently disentangles lanes without being constrained by fixed class numbers, addressing the lane change problem effectively.

Perspective Transformation and Lane Fitting

To convert lane pixels into a parametric description, a perspective transformation is employed. Traditional methods use a fixed "bird's-eye view" transformation, leading to inaccuracies on varying road planes. This paper introduces H-Net, a network trained to predict a perspective transformation matrix conditioned on the input image. This approach allows lane fitting that accounts for road plane variations, yielding more robust lane parameterization.

Experimental Results

The proposed approach was tested on the tuSimple dataset, demonstrating competitive results with an accuracy of 96.4%. The model operates at a notable speed of 50 fps, showcasing its real-time applicability. Notably, the perspective transformation generated by H-Net improved the mean squared error in lane fitting when compared to fixed transformations, displaying enhanced accuracy under different road conditions.

Practical Implications and Future Research

The paper's contributions lie not only in addressing the computational and adaptability issues present in previous methods but also in enhancing the robustness of lane detection solutions in autonomous vehicles. The use of instance segmentation allows for a dynamic lane detection approach that can adjust to complex road scenarios.

Future research could explore extending the capabilities of LaneNet to more diverse driving environments, including varied lighting conditions and adverse weather. Additionally, integrating this approach with multi-modal sensor data could further bolster the detection capabilities, enhancing overall vehicle autonomy and safety.

In conclusion, this paper advances the domain of lane detection by leveraging instance segmentation, paving the way for more flexible and robust autonomous vehicle systems.