Dataset Information

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving.

ABSTRACT: Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated.

SUBMITTER: Magosi ZF

PROVIDER: S-EPMC9370944 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving.

Magosi Zoltan Ferenc ZF Li Hexuan H Rosenberger Philipp P Wan Li L Eichberger Arno A

Sensors (Basel, Switzerland) 20220729 15

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency ...[more]

PMID: 35957250

Similar Datasets

Project description:BackgroundVisuospatial memory impairment is a common symptom of Alzheimer's disease; however, conventional visuospatial memory tests are insufficient to fully reflect visuospatial memory impairment in daily life.MethodsTo address patients' difficulties in locating and recalling misplaced objects, we introduced a novel visuospatial memory test, the Hidden Objects Test (HOT), conducted in a virtual environment. We categorized HOT scores into prospective memory, item free-recall, place free-recall, item recognition, and place-item matching scores. To validate the VR memory test, we compared HOT scores among individuals with Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI), and normal controls (NC), and also compared these scores with those of conventional neuropsychological tests. We tracked the participants' movement paths in the virtual environment and assessed basic features, such as total distance, duration, and speed. Additionally, we performed walking trajectory pattern mining such as outlier and stay-point detection.ResultsWe designed and implemented the HOT to simulate a house's living room and assess participants' ability to locate hidden objects. Our preliminary results showed that the total HOT score differed among 17 patients with AD, 14 with aMCI, and 15 NC (p < 0.001). The total HOT score correlated positively with conventional memory test scores (p < 0.001). Walking trajectories showed that patients with AD and aMCI wandered rather than going straight to the hidden objects. In terms of basic features, the total duration was significantly greater in AD than in NC (p = 0.008). In terms of trajectory pattern mining, the number of outliers, which were over 95% of the estimated trajectory, was significantly higher in AD than in NC (p = 0.002). The number of stay points, an index in which participants stayed in the same position for more than 2 s, was significantly higher in patients with AD and aMCI compared with NC (AD vs. NC: p = 0.003, aMCI vs. NC: p = 0.019).ConclusionThe HOT simulating real life showed potential as an ecologically valid test for assessing visuospatial memory function in daily life. Walking trajectory analysis suggested that patients with AD and aMCI wandered rather than going straight toward the hidden objects.

Dataset Information

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving.

Publications

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets