16 accepted papers
Polar coordinates are widely used in segmentation tasks for range sensors such as LiDAR and radar, owing to their ability to naturally align with point cloud sparsity and distribution. However, their use in detection is limited by feature distortion. Existing polar-based detection works focused on u…
Moving object segmentation (MOS) is essential for autonomous driving, enabling robust detection, tracking, and prediction of dynamic agents in complex traffic scenarios. Radar sensors offer notable advantages for long-range sensing, but their lower spatial resolution, measurement noise, and geometri…
Traffic sign detection plays an essential role in advanced driver assistance system (ADAS) or self-driving vehicles. Typically, deep neural networks are employed to analyze road scene images captured by an onboard camera. However, due to the significant variation in appearance of different traffic s
Scanning LiDAR is one of the widely used sensors in autonomous vehicles; however, the inherent sparsity of LiDAR point clouds often affects its performance. To address this issue, upsampling methods could be employed to enhance low-resolution LiDAR data. Although there have been methods on upsamplin…
Recent years have seen a shift towards learning-based methods for trajectory prediction, with challenges remaining in addressing uncertainty and capturing multi-modal distributions. This paper introduces Temporal Ensembling with Learning-based Aggregation, a meta-algorithm designed to mitigate the i…
In driving scenarios, motion segmentation is a crucial and fundamental component that is needed for many tasks. Recently, a self-supervised multitasking framework was proposed for driving scenarios. It simultaneously trains motion segmentation, optical flow, depth, and ego-motion models without anno…
We propose LiDAR-Inertial Odometry via Simultaneous EGo-motion estimation and Multiple Object Tracking (LIO-SEGMOT), an optimization-based odometry approach targeted for dynamic environments. LIO-SEGMOT is formulated as a state estimation approach with asynchronous state update of the odometry and t…
Automotive radars have been reliably used in most assisted and autonomous driving systems due to their robustness to extreme weather conditions. With radial velocity measurements from automotive radars, moving targets such as cars, trucks, and buses can be tracked robustly. However, due to the lack…
LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be ben…
Occlusion remains an issue in multiple object tracking, which could cause ambiguity in object detection, such as incorrect or missing detection. Under occlusion, a track could experience an early termination, resulting in identity switches and/or fragmentation. To recover from different lengths of o…
We propose K-Closest Points (KCP), an efficient and effective laser scan matching approach inspired by LOAM and TEASER++. The efficiency of KCP comes from a feature point extraction approach utilizing the multi-scale curvature and a heuristic matching method based on the <inline-formula xmlns:mml="h
Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupiedspaces in radar ima
Lidars are commonly used on autonomous vehicles, but their performance can be significantly affected by adverse weather. A number of studies have been devoted to analyzing and improving lidars’ performance in rain, fog, and snow. Yet, relatively little attention has been paid to road spray which occ
Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholdin…
While automotive radars are widely used in most assisted and autonomous driving systems, only a few works were proposed to tackle the calibration problems of automotive radars with other perception sensors. One of the key calibration challenges of automotive planar radars with other sensors is the m…
This work aims to accurately match two scene images under large viewpoint changes, which is the key issue in appearance-based localization tasks. In this paper, two key ideas are proposed to solve the challenging problem. First, to detect extreme small overlapping regions between two images, a new a…