Machine learning method to ensure robust decision-making of AVs
| Autor: | Ramdane Tami, Javier Ibanez, Justin Dauwels, Abdelkrim Doufene, Boussaad Soualmi |
|---|---|
| Přispěvatelé: | Nanyang Technological University, Energy Research Institute at NTU (ERIAN), Nanyang Technological University [Singapour], IRT SystemX (IRT SystemX), Technocentre Renault [Guyancourt], RENAULT, School of Electrical and Electronic Engineering, Nanyang |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Computer science business.industry 05 social sciences 02 engineering and technology Machine learning computer.software_genre Robust decision-making [SPI.AUTO]Engineering Sciences [physics]/Automatic [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Software deployment Robustness (computer science) [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | ITSC 2019 ITSC 2019, Oct 2019, Auckland, New Zealand ITSC |
| Popis: | International audience; Replacing the human driver to perform the Dynamic Driving Task (DDT)[1] will require perception, complex analysis and assessment of traffic situation. The path leading to success the deployment of fully Autonomous Vehicle (AV) depends on the resolution of a lot of challenges. Both the safety and the security aspects of AV constitute the core of regulatory compliance and technical research. The Autonomous Driving System (ADS) should be designed to ensure a safe manoeuvre and a stable behaviour despite the technological limitations, the uncertainties and hazards which characterize the real traffic conditions. In fully Autonomous Driving situation, detecting all relevant objects and agents should be sufficient to generate a warning, however the ADS requires further complex data analysis steps to quantify and improve the safety of decision making. This paper aims to improve the robustness of decision-making in order to mimic human-like decision ability. The approach is based on machine learning to identify the criticality of the dynamic situation and enabling ADS to make appropriate decision and fulfil safe manoeuvre. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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