Online Quantitative Analysis of Perception Uncertainty Based on High-Definition Map.

Environmental perception plays a fundamental role in decision-making and is crucial for ensuring the safety of autonomous driving. A pressing challenge is the online evaluation of perception uncertainty, a crucial step towards ensuring the safety and the industrialization of autonomous driving. High-definition maps offer precise information about static elements on the road, along with their topological relationships.

Real-Time Evaluation of Perception Uncertainty and Validity Verification of Autonomous Driving.

Deep neural network algorithms have achieved impressive performance in object detection. Real-time evaluation of perception uncertainty from deep neural network algorithms is indispensable for safe driving in autonomous vehicles. More research is required to determine how to assess the effectiveness and uncertainty of perception findings in real-time.

MMW Radar-Based Technologies in Autonomous Driving: A Review.

With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving.

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs.

Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system.

Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids.

Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space.

Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data.

Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult.

A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles.

Future intelligent transport systems depend on the accurate positioning of multiple targets in the road scene, including vehicles and all other moving or static elements. The existing self-positioning capability of individual vehicles remains insufficient. Also, bottlenecks in developing on-board perception systems stymie further improvements in the precision and integrity of positioning targets.

Complete de-Dopplerization and acoustic holography for external noise of a high-speed train.

Identification and measurement of moving sound sources are the bases for vehicle noise control. Acoustic holography has been applied in successfully identifying the moving sound source since the 1990s. However, due to the high demand for the accuracy of holographic data, currently the maximum velocity achieved by acoustic holography is just above 100 km/h.