Enhancing Autonomous Driving through 4D-BEV Annotation

Recently, AI technology’s application covers many fields, from smart security to autonomous driving. And behind every achievement is inseparable from strong data support. As the core factor of AI algorithm, datasets aren’t just the basis for model training, but also the key factor for improving mode performance, By continuously collecting and labeling various datasets, developer can accomplish application with more smarter, efficient system.

Bird's Eye View (BEV) offers a panoramic, god's-eye perspective by consolidating data from various vehicle sensors into a unified model. This high-level view mitigates error accumulation in data fusion, a crucial aspect for autonomous driving. Aligning coordinate systems within BEV creates a 4D space, integrating the time dimension to enhance object trajectory tracking.

However, annotating 4D BEV data presents challenges due to its complexity. Enter 4D-BEV annotation technology—an innovative solution pioneered by Nexdata. This groundbreaking approach introduces the time axis into ai data annotation, facilitating more robust AI systems. Annotators leverage BEV to label static objects like vehicles and pedestrians, recording their attributes alongside entry and exit times. This detailed ai data service significantly bolsters safety and decision-making algorithms in autonomous vehicles.

Nexdata's 4D annotation tool operates in a 3D space with an added temporal dimension. It seamlessly integrates data from lidar, radar, cameras, and overhead views while ensuring precise alignment and fusion. Notably, the tool's pre-recognition annotation technology substantially boosts both efficiency and accuracy in labeling.

Key Features of Nexdata's Annotation Tool

One standout feature is its ability to handle massive point cloud data collection and annotation seamlessly. The utilization of Potree, a WebGL-based framework, enables interactive visualization of extensive point cloud data on web platforms. To avoid biases, the tool references mapping parameters from AI data collections. Additionally, customizable color settings facilitate accurate identification of point cloud targets, while the preloading function further enhances annotation efficiency.

Case Studies Illustrating Utility

4D Lane Marking Annotation: This process involves annotating lidar point cloud data annotaion services with global pose information, specifically focusing on lane markings, including solid, dashed, double lines, and guide lines.

4D Segmentation Annotation: Reconstruction of sequential frames to annotate semantic segmentation, categorizing areas like vegetation, drivable zones, and unknown obstacles.

Nexdata's extensive collaborations with global autonomous driving entities, spanning OEMs, startups, tech giants, and Tier1 manufacturers, underscore its expertise in this field. Their commitment to continuous technical development aims to refine AI infrastructure, simplifying the deployment of AI applications.

Looking ahead, Nexdata remains dedicated to advancing technical research, further improving AI infrastructure, and supporting users in deploying AI applications more conveniently.

With the in-depth application of artificial intelligence, the value of data has become prominent. Only with the support of massive high-quality data can AI technology breakthrough its bottlenecks and advance in a more intelligent and efficient direction. In the future, we need to continue to explore new ways of data collection and annotation to better cope with complex business requirements and achieve intelligent innovation.