11 accepted papers
3D occupancy prediction provides dense spatial understanding critical for safe autonomous driving. However, this task suffers from a severe class imbalance due to its volumetric representation, where safety-critical objects (bicycles, traffic cones, pedestrians) occupy minimal voxels compared to dom
Recently, camera-radar fusion-based 3D object detection methods in bird's eye view (BEV) have gained attention due to the complementary characteristics and cost-effectiveness of these sensors. Previous approaches using forward projection struggle with sparse BEV feature generation, while those emplo
Vision-based 3D occupancy prediction has made significant advancements, but its reliance on cameras alone struggles in challenging environments. This limitation has driven the adoption of sensor fusion, among which camera-radar fusion stands out as a promising solution due to their complementary str
"Predicting the maneuvers of surrounding vehicles is imperative for the safe navigation of autonomous vehicles. However, naturalistic driving datasets tend to be highly imbalanced, with a bias towards the ”going straight” maneuver. Consequently, learning and accurately predicting turning maneuvers p…
Embodied AI such as autonomous vehicles suffers from insufficient long-tailed data because it must be obtained from the physical world. In fact data must be continuously obtained in a series of small batches and the model must also be continuously trained to achieve generalizability and scalability…
"Recent advancements in camera-based 3D object detection have introduced cross-modal knowledge distillation to bridge the performance gap with LiDAR 3D detectors, leveraging the precise geometric information in LiDAR point clouds. However, existing cross-modal knowledge distillation methods tend to…
Camera and radar sensors have significant advantages in cost, reliability, and maintenance compared to LiDAR. Existing fusion methods often fuse the outputs of single modalities at the result-level, called the late fusion strategy. This can benefit from using off-the-shelf single sensor detection al…
Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation. Although recent low-cost camera-based approaches have shown promising results, they are susceptible to poor illumination or bad weather conditions and have a large loc…
Prior studies in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory, which is highly biased toward straight maneuvers. Such problem formulations and imbalanced distribution of datasets, however, frequently lead to
Autonomous driving powered by deep learning requires large-scale, high-quality training data from diverse driving environments to operate effectively worldwide. However, collecting and annotating such data is costly and time-consuming. To address this challenge, active learning methods have been exp…
Recent camera-based 3D object detection methods have introduced sequential frames to improve the detection performance hoping that multiple frames would mitigate the large depth estimation error. Despite improved detection performance, prior works rely on naive fusion methods (e.g., concatenation) o…