arXiv subjects

Tim Brühl

Publications and source records attributed to Tim Brühl.

3 recordsLinked to original sources

arXiv2026

Improving Driver Satisfaction with a Driving Function Learning from Implicit Human Feedback -- a Test Group Study

During the use of advanced driver assistance systems, drivers frequently intervene into the active driving function and adjust the system's behavior to their personal wishes. These active driver-initiated takeovers contain feedback about deviations in the driving function's behavior from the drivers' personal preferences. This feedback should be utilized to optimize and personalize the driving function's behavior. In this work, the adjustment of the speed profile of a Predictive Longitudinal Driving Function (PLDF) on a pre-defined route is highlighted. An algorithm is introduced which iteratively adjusts the PLDF's speed profile by taking into account both the original speed profile of the PLDF and the driver demonstration. This approach allows for personalization in a traded control scenario during active use of the PLDF. The applicability of the proposed algorithm is tested in a driving simulator-based test group study with 43 participants. The study finds a significant increase in driver satisfaction and a significant reduction in the intervention frequency when using the proposed adaptive PLDF. Additionally, feedback by the participants was gathered to identify further optimization potentials of the proposed system.

cs.RO↗

arXiv2025

SAFERad: A Framework to Enable Radar Data for Safety-Relevant Perception Tasks

Radar sensors play a crucial role for perception systems in automated driving but suffer from a high level of noise. In the past, this could be solved by strict filters, which remove most false positives at the expense of undetected objects. Future highly automated functions are much more demanding with respect to error rate. Hence, if the radar sensor serves as a component of perception systems for such functions, a simple filter strategy cannot be applied. In this paper, we present a modified filtering approach which is characterized by the idea to vary the filtering depending on the potential of harmful collision with the object which is potentially represented by the radar point. We propose an algorithm which determines a criticality score for each point based on the planned or presumable trajectory of the automated vehicle. Points identified as very critical can trigger manifold actions to confirm or deny object presence. Our pipeline introduces criticality regions. The filter threshold in these criticality regions is omitted. Commonly known radar data sets do not or barely feature critical scenes. Thus, we present an approach to evaluate our framework by adapting the planned trajectory towards vulnerable road users, which serve as ground truth critical points. Evaluation of the criticality metric prove high recall rates. Besides, our post-processing algorithm lowers the rate of non-clustered critical points by 74.8 % in an exemplary setup compared to a moderate, generic filter.

eess.SP↗

arXiv2022

Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night

Provident detection of other road users at night has the potential for increasing road safety. For this purpose, humans intuitively use visual cues, such as light cones and light reflections emitted by other road users to be able to react to oncoming traffic at an early stage. This behavior can be imitated by computer vision methods by predicting the appearance of vehicles based on emitted light reflections caused by the vehicle's headlights. Since current object detection algorithms are mainly based on detecting directly visible objects annotated via bounding boxes, the detection and annotation of light reflections without sharp boundaries is challenging. For this reason, the extensive open-source dataset PVDN (Provident Vehicle Detection at Night) was published, which includes traffic scenarios at night with light reflections annotated via keypoints. In this paper, we explore the potential of saliency-based approaches to create different object representations based on the visual saliency and sparse keypoint annotations of the PVDN dataset. For that, we extend the general idea of Boolean map saliency towards a context-aware approach by taking into consideration sparse keypoint annotations by humans. We show that this approach allows for an automated derivation of different object representations, such as binary maps or bounding boxes so that detection models can be trained on different annotation variants and the problem of providently detecting vehicles at night can be tackled from different perspectives. With that, we provide further powerful tools and methods to study the problem of detecting vehicles at night before they are actually visible.

cs.CV↗