3D-LaneNet: End-to-End 3D Multiple Lane Detection (1811.10203v3)

Abstract: We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image. This work marks a first attempt to address this task with on-board sensing without assuming a known constant lane width or relying on pre-mapped environments. Our network architecture, 3D-LaneNet, applies two new concepts: intra-network inverse-perspective mapping (IPM) and anchor-based lane representation. The intra-network IPM projection facilitates a dual-representation information flow in both regular image-view and top-view. An anchor-per-column output representation enables our end-to-end approach which replaces common heuristics such as clustering and outlier rejection, casting lane estimation as an object detection problem. In addition, our approach explicitly handles complex situations such as lane merges and splits. Results are shown on two new 3D lane datasets, a synthetic and a real one. For comparison with existing methods, we test our approach on the image-only tuSimple lane detection benchmark, achieving performance competitive with state-of-the-art.

• The paper introduces 3D-LaneNet, an end-to-end deep CNN for 3D multiple lane detection from a single image, using a dual-pathway architecture and anchor-based representation.
• Evaluated on synthetic and real-world datasets, 3D-LaneNet achieved an AP of 0.952 on synthetic data and demonstrated robustness when transferring to real-world conditions.
• This approach significantly advances autonomous driving by providing accurate 3D lane modeling from onboard sensing without relying on pre-existing maps or geometric assumptions.

3D-LaneNet: End-to-End 3D Multiple Lane Detection

The paper presents an innovative approach for 3D multiple lane detection, introducing a deep convolutional neural network named 3D-LaneNet. This network predicts the 3D layout of road lanes from a single image, utilizing concepts like intra-network inverse-perspective mapping (IPM) and anchor-based lane representation. Unlike traditional methods that assume constant lane width or rely on pre-existing maps, 3D-LaneNet functions with onboard sensing and offers accurate lane detection without geometric assumptions.

Methodology

3D-LaneNet operates on a dual-pathway architecture. The image-view pathway captures spatial image features, while the top-view pathway allows translation invariance, crucial for understanding lanes in diverse orientations and curvatures. Intra-network IPM transforms image feature maps to a top-view perspective, facilitating more precise 3D estimation.

The network applies an anchor-based lane representation, akin to object detection models such as SSD and YOLO. Each lane is treated like an object, with specific anchors facilitating lateral positioning. This end-to-end approach for lane estimation negates the need for post-processing techniques such as clustering and outlier rejection, commonly seen in existing lane detection methods.

Results

The network was tested on two datasets: synthetic and real-world. The synthetic dataset was generated programmatically with randomized variability in road shapes, lane topologies, and object placements, providing around 300,000 training examples. The real-world dataset, 3D-lanes, consisted of image data acquired from vehicle-mounted cameras complemented by Lidar-based ground truth. Evaluation metrics include average precision (AP) and Euclidean distance-based error for geometric accuracy, yielding promising results competitive with state-of-the-art methods.

On synthetic datasets, 3D-LaneNet achieved an AP of 0.952 for centerline detection with notable geometric accuracy, demonstrating the importance of a dual-pathway architecture over solely image or top views. On real-world data, the detection results demonstrated the robustness of 3D-LaneNet in transferring learning from synthetic data to varied real-world conditions.

Implications & Future Work

The implications of this research are significant for autonomous driving technologies, where accurate 3D lane modeling enhances vehicle navigation and safety. By eliminating the reliance on pre-mapped environments or static geometric assumptions, this approach represents a substantial step forward in real-time driving scenarios.

Future developments could explore integrating this approach with additional vision-based tasks, such as 3D vehicle and object detection, utilizing the dual-pathway architecture. Incorporating dynamic environmental elements and addressing complex urban intersections could further refine the network's capabilities, broadening its application range within autonomous vehicle systems.

Overall, 3D-LaneNet provides a robust foundation for advancing perception-based driving systems, highlighting potential avenues for future exploration in AI-driven autonomous navigation technologies.