Addressing Data Annotation Challenges in Multiple Sensors: A Solution for Scania Collected Datasets

Data annotation in autonomous vehicles is a critical step in the development of Deep Neural Network (DNN) based models or the performance evaluation of the perception system. This often takes the form of adding 3D bounding boxes on time-sequential and registered series of point-sets captured from active sensors like Light Detection and Ranging (LiDAR) and Radio Detection and Ranging (RADAR). When annotating multiple active sensors, there is a need to motion compensate and translate the points to a consistent coordinate frame and timestamp respectively. However, highly dynamic objects pose a unique challenge, as they can appear at different timestamps in each sensor's data. Without knowing the speed of the objects, their position appears to be different in different sensor outputs. Thus, even after motion compensation, highly dynamic objects are not matched from multiple sensors in the same frame, and human annotators struggle to add unique bounding boxes that capture all objects. This article focuses on addressing this challenge, primarily within the context of Scania collected datasets. The proposed solution takes a track of an annotated object as input and uses the Moving Horizon Estimation (MHE) to robustly estimate its speed. The estimated speed profile is utilized to correct the position of the annotated box and add boxes to object clusters missed by the original annotation.

Fully Sparse Long Range 3D Object Detection Using Range Experts and Multimodal Virtual Points

3D object detection at long-range is crucial for ensuring the safety and efficiency of self-driving cars, allowing them to accurately perceive and react to objects, obstacles, and potential hazards from a distance. But most current state-of-the-art LiDAR based methods are limited by the sparsity of range sensors, which generates a form of domain gap between points closer to and farther away from the ego vehicle. Another related problem is the label imbalance for faraway objects, which inhibits the performance of Deep Neural Networks at long-range. Although image features could be beneficial for long-range detections, and some recently proposed multimodal methods incorporate image features, they do not scale well computationally at long ranges or are limited by depth estimation accuracy. To address the above limitations, we propose to combine two LiDAR based 3D detection networks, one specializing at near to mid-range objects, and one at long-range 3D detection. To train a detector at long range under a scarce label regime, we further propose to weigh the loss according to the labelled objects' distance from ego vehicle. To mitigate the LiDAR sparsity issue, we leverage Multimodal Virtual Points (MVP), an image based depth completion algorithm, to enrich our data with virtual points. Our method, combining two range experts trained with MVP, which we refer to as RangeFSD, achieves state-of-the-art performance on the Argoverse2 (AV2) dataset, with improvements at long range. The code will be released soon.

Semantic 3D Grid Maps for Autonomous Driving

Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is three-dimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.