Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection (2010.12035v2)

Abstract: Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles. In this work, we propose LaneATT: an anchor-based deep lane detection model, which, akin to other generic deep object detectors, uses the anchors for the feature pooling step. Since lanes follow a regular pattern and are highly correlated, we hypothesize that in some cases global information may be crucial to infer their positions, especially in conditions such as occlusion, missing lane markers, and others. Thus, this work proposes a novel anchor-based attention mechanism that aggregates global information. The model was evaluated extensively on three of the most widely used datasets in the literature. The results show that our method outperforms the current state-of-the-art methods showing both higher efficacy and efficiency. Moreover, an ablation study is performed along with a discussion on efficiency trade-off options that are useful in practice.

• The paper introduces LaneATT, a novel single-stage model that combines anchor-based feature pooling with an attention mechanism for robust lane detection.
• It employs CNNs with fixed y-coordinate lane representation to effectively aggregate local and global contextual information in real time.
• Evaluated on multiple datasets, LaneATT achieves up to 96.71% F1 and runs at 250 FPS, significantly reducing computational requirements in adverse conditions.

Insights into Real-time Attention-guided Lane Detection

The paper under examination introduces "LaneATT," a novel real-time lane detection model designed to enhance the efficiency and accuracy of lane detection systems in autonomous driving. By integrating an anchor-based detection system with a cutting-edge attention mechanism, this model provides critical advancements in handling the challenging conditions that often arise in real-world driving scenarios.

Model Overview and Strategy

LaneATT is a single-stage detection model that parallels established deep object detection methods like YOLOv3 and SSD. It operates by leveraging anchor-based feature pooling alongside an innovative attention mechanism. This mechanism facilitates the aggregation of global contextual information – a factor vital for accurately detecting lane positions when encountering occlusion, missing markers, or complex light conditions.

The methodology for lane representation in LaneATT employs 2D-points with fixed y-coordinates, which allow the lane to be defined merely by its x-coordinates. Anchors, represented as virtual lines within the image plane, form pivotal reference points in this detection process. By utilizing deep convolutional neural networks (CNNs) as its backbone, the LaneATT model efficiently pools features projected by anchors and combines them with global attention-enhanced features, thereby supporting both local and global information incorporation for superior lane detection performance.

Performance Metrics and Results

Evaluated comprehensively on TuSimple, CULane, and LLAMAS datasets, LaneATT demonstrates superior performance against state-of-the-art models. Notably, it reaches 96.71% F1 on the TuSimple dataset, and 77.02% on CULane, achieving significant benchmarks particularly under challenging conditions such as nighttime and shadowed environments.

Moreover, the LaneATT model shows a profound reduction in processing requirements, operating at 250 FPS with ResNet-18, despite having nearly an order of magnitude fewer MACs than comparable models. The proposed attention mechanism further enhances the model's robustness, as indicated by the substantial performance gains shown during ablation studies—exemplifying its efficiency in overcoming the constraints of traditional deep learning models in autonomous driving.

Practical Implications and Future Directions

With LaneATT, the interplay between robust feature extraction and efficient computation provides critical groundwork for real-time applications in intelligent transportation systems. The integration of attention mechanisms in anchor-based models opens new avenues for improving autonomous vehicle perception under adverse conditions, ensuring improved navigation and safety.

Future directions potentially include refining the attention mechanism for broader object detection applications and further enhancing the model's efficiency across more diverse and complex driving datasets. Building on these findings, the research underscores the significant contributions of global information aggregation in facilitating precise, real-time lane detection, poised to advance the applications of autonomous driving technologies significantly.

Conclusively, "Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection" sets a new benchmark in lane detection technologies. By addressing existing limitations in efficiency and accuracy, this paper delineates an innovative pathway for future research in intelligent vehicle systems.