With the rapid development of autonomous driving technology, ensuring the safety and
reliability of vehicles under various complex and adverse conditions has become …

The detection of multiple extended targets in complex environments using high-resolution
automotive radar is considered. A data-driven approach is proposed where unlabeled …

This paper presents a novel deep-learning-based approach to improve localizing radar
measurements against lidar maps. This radar-lidar localization leverages the benefits of …

LiDAR sensors are crucial for providing high-resolution 3D point cloud data in autonomous
driving systems, enabling precise environmental perception. However, real-world adverse …

Context information provided along with a dataset can be very helpful for solving a problem
because the additional knowledge is already available and does not need to be extracted …

Many companies and institutions focus on autonomous vehicles. Accordingly, the
commercialization of fully autonomous vehicles is expected to proceed rapidly. Autonomous …

A key element of many odometry pipelines using spinning frequency-modulated continuous-
wave (FMCW) radar is the extraction of a point-cloud from the raw signal. This extraction …

Pedestrians are among the most endangered traffic participants in road traffic. While
pedestrian detection in nominal conditions is well established, the sensor and, therefore, the …

The development of machine learning based perception algorithms for safety-critical
applications such as automated driving systems requires controlled environments for data …

Reliable offroad autonomy requires low-latency, high-accuracy state estimates of pose as
well as velocity, which remain viable throughout environments with sub-optimal operating …