Autonomous Driving

Rethinking 3D Geometric Feature Learning for Neural Reconstruction

Abstract: Recent advances in neural reconstruction using posed image sequences have made remarkable progress. However, due to the lack of depth information, existing volumetric-based techniques simply duplicate 2D image features of the object surface along the entire camera ray.

Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth

Abstract: Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.

Advancing Self-Supervised Monocular Depth Learning with Sparse LiDAR

Abstract: Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for autonomous robots.

AMMF: Attention-Based Multi-Phase Multi-Task Fusion for Small Contour Object 3D Detection

Abstract: Recently significant progress has been made in 3D detection. However, it is still challenging to detect small contour objects under complex scenes. This paper proposes a novel Attention-based Multi-phase Multi-task Fusion (AMMF) that uses point-level, RoI-level, and multi-task fusions to complement the disadvantages of LiDAR and camera, to solve this challenge.

PSE-Match: A Viewpoint-Free Place Recognition Method With Parallel Semantic Embedding

Abstract: Accurate localization on the autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments where high-accurate GPS is not available. However current odometry estimation may introduce the drifting problems in long-term navigation without robust global localization.

Multi-Scale Fusion With Matching Attention Model: A Novel Decoding Network Cooperated With NAS for Real-Time Semantic Segmentation

Abstract: This paper proposes a real-time multi-scale semantic segmentation network (MsNet). MsNet is a combination of our novel multi-scale fusion with matching attention model (MFMA) as the decoding network and the network searched by asymptotic neural architecture search (ANAS) or MobileNetV3 as the encoding network.

Nondestructive Evaluation of Terrain Using mmWave Radar Imaging

Abstract: Military ground vehicles operate in off-road environments traversing different terrains under various environmental conditions. There has been an increasing interest towards autonomous off-road vehicle navigation, leading to the needs of terrain traversability assessment through sensing.

Semantic Digital Surface Map Towards Collaborative Off-Road Vehicle Autonomy

Abstract: The fundamental aspect of unmanned ground vehicle (UGV) navigation, especially over off-road environments, are representations of terrain describing geometry, types, and traversability. One of the typical representations of the environment is digital surface models (DSMs) which efficiently encode geometric information.

Dynamic Modeling and Prediction of Rollover Stability for All-Terrain Vehicles

Abstract: With the particular passage capability, all-terrain vehicle (ATV) has been widely used for off-road scenarios. In this research, we conduct a lateral sway stability analysis for the suspension mechanism of a general vehicle and establish a mathematical model of static and dynamic stability based on the maximum lateral sway angle and lateral sway acceleration, by considering the combined angular stiffness of independent suspension, angular stiffness of the lateral stabilizer bar and vertical stiffness of tires.

Driver Drowsiness Behavior Detection and Analysis Using Vision-Based Multimodal Features for Driving Safety

Abstract: Driving inattention caused by drowsiness has been a significant reason for vehicle crash accidents, and there is a critical need to augment driving safety by monitoring driver drowsiness behaviors. For real-time drowsy driving awareness, we propose a vision-based driver drowsiness monitoring system (DDMS) for driver drowsiness behavior recognition and analysis.