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4D mmWave radar sensors are suitable for roadside perception in city-scale Intelligent Transportation Systems (ITS) due to their long sensing range, weatherproof functionality, simple mechanical design, and low manufacturing cost. In this…
This paper introduces GRAD, a real-time anomaly detection method for autonomous vehicle sensors that integrates statistical analysis and deep learning to ensure the reliability of sensor data. The proposed approach combines the Reinforced…
Semantic understanding of roadways is a key enabling factor for safe autonomous driving. However, existing autonomous driving datasets provide well-structured urban roads while ignoring unstructured roadways containing distress, potholes,…
A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive…
Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance plans and more. Traditional methods of quantifying vehicle volume rely on manual…
This paper summarizes the design, experiments and results of our solution to the Road Damage Detection and Classification Challenge held as part of the 2018 IEEE International Conference On Big Data Cup. Automatic detection and…
While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limited. As they have the ability to resolve multiple targets in…
GPS receivers embedded in cell phones and connected vehicles generate a series of location measurements that can be used for various analytical purposes. A common pre-processing step of this data is the so-called map matching. The goal of…
High definition (HD) map needs to be updated frequently to capture road changes, which is constrained by limited specialized collection vehicles. To maintain an up-to-date map, we explore crowdsourcing data from connected vehicles. Updating…
A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, we present ORAD-3D, which, to the best of our knowledge, is the largest dataset…
Despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges. The absence of precise camera calibration data, including intrinsic and extrinsic…
Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations…
Mobility On Demand (MOD) systems are revolutionizing transportation in urban settings by improving vehicle utilization and reducing parking congestion. A key factor in the success of an MOD system is the ability to measure and respond to…
Highly automated driving functions currently often rely on a-priori knowledge from maps for planning and prediction in complex scenarios like cities. This makes map-relative localization an essential skill. In this paper, we address the…
The growing use of probe vehicles generates a huge number of GNSS data. Limited by the satellite positioning technology, further improving the accuracy of map-matching is challenging work, especially for low-frequency trajectories. When…
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for developing reliable autonomous driving algorithms. Previous…
Detecting road obstacles is essential for autonomous vehicles to navigate dynamic and complex traffic environments safely. Current road obstacle detection methods typically assign a score to each pixel and apply a threshold to generate…
Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular…
Localizing ground devices (GDs) is an important requirement for a wide variety of applications, such as infrastructure monitoring, precision agriculture, search and rescue operations, to name a few. To this end, unmanned aerial vehicles…
The validation and verification of automated driving functions (ADFs) is a challenging task on the journey of making those functions available to the public beyond the current research context. Simulation is a valuable building block for…