Large-Scale Evaluation of an Active Safety Algorithm with EuroNCAP and US NCAP Scenarios in a Virtual Test Environment -- An Industrial Case Study

Berger, Christian; Block, Delf; Hons, Christian; Kühnel, Stefan; Leschke, André; Plotnikov, Dimitri; Rumpe, Bernhard

doi:10.1109/itsc.2015.368

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…However, AD vehicles have a requirement of competence to cope with all-weather environments and extreme situations. Only systems that apply MSHIF can perform 100% recognition accuracy, thus achieving the best European NCAP security level [48], [67].…”

Section: B Cameramentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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With the significant development of practicability in deep learning and the ultra-highspeed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

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Section: B Cameramentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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Lane Change Classification with Neural Networks for Automated Conversion of Logical Scenarios

Diklić¹,

Marković

2023

Lecture Notes in Networks and Systems

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Performance Evaluation Method for Automated Driving System in Logical Scenario

Zhang

Zhu²,

Zhao³

et al. 2022

Automot. Innov.

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With the continuous improvement of automated driving technology, how to evaluate the performance of an automated driving system is attracting more and more attention. Meanwhile, with the creation of scenario-based test methods, the traditional evaluation index based on a single test can no longer meet the requirements of high-level safety verification for automated driving system, and the performance evaluation of such a system in logical scenarios will be the mainstream. Based on the scenario-based test method and Turing test theory, a performance evaluation method for an automated driving system in the whole parameter space of a logical scenario is proposed. The logical scenario parameter space is partitioned according to the risk degree of concrete scenario, and the evaluation process in different zones are determined. Subsequently, the anthropomorphic index in the safe zone and the collision-avoidance index in the danger zone are defined by comparing test results of human driving and ideal vehicle motion. Taking front vehicle low-speed and cut-out scenarios as examples, two automated driving algorithms are tested in the virtual environment, and the test results are evaluated both by the proposed method and by human observation. The results show that the results of the proposed method are consistent with the subjective feelings of humans; additionally, it can be applied to scenario-based tests and the verification process of an automated driving system.

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Large-Scale Evaluation of an Active Safety Algorithm with EuroNCAP and US NCAP Scenarios in a Virtual Test Environment -- An Industrial Case Study

Cited by 7 publications

References 14 publications

Multi-Sensor Fusion in Automated Driving: A Survey

Multi-Sensor Fusion in Automated Driving: A Survey

Lane Change Classification with Neural Networks for Automated Conversion of Logical Scenarios

Performance Evaluation Method for Automated Driving System in Logical Scenario

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