A Unified Evaluation Framework for Autonomous Driving Vehicles
| Autor: | Ankur Agrawal, Danson Evan Garcia, Mohammed Elmahgiubi, Myada Roshdi, Nasif Nayeer |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
0209 industrial biotechnology Computer science Reliability (computer networking) media_common.quotation_subject 05 social sciences Fidelity 02 engineering and technology Test (assessment) Software development process 020901 industrial engineering & automation End-to-end principle Software deployment Safety assurance 0502 economics and business Systems engineering Test data media_common |
| Zdroj: | 2020 IEEE Intelligent Vehicles Symposium (IV). |
| Popis: | Automated Driving System (ADS) safety assessment is a crucial step before deployment on public roads. Despite the importance of ADS safety assurance to test ADS reliability, most of the existing work is strongly attached to a single testing data source (i.e. on-road collected testing data, simulation or test track). Each source has different fidelity levels and capabilities, therefore there is a lack of a solution that allows for all data sources to complement each other to enable agnostic end to end evaluation and contributes towards different testing goals. Evaluation of ADSs is considered as a mandatory step in the autonomous vehicle development life cycle, demanding a reliable and comprehensive method is important. Here, we propose a source-agnostic framework, which can perform ADS evaluation compatible with different testing sources. Our findings show that this comprehensive solution can save time, effort and money consumed in ADS evaluation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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