Novel Decision-Making Strategy for Connected and Autonomous Vehicles in Highway On-Ramp Merging
| Autor: | Zine El Abidine Kherroubi, Samir Aknine, Rebiha Bacha |
|---|---|
| Přispěvatelé: | Technocentre Renault [Guyancourt], RENAULT, Systèmes Cognitifs et Systèmes Multi-Agents (SyCoSMA), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2) |
| Rok vydání: | 2022 |
| Předmět: |
050210 logistics & transportation
Artificial neural network Computer science Mechanical Engineering 05 social sciences Real-time computing Computer Science Applications Acceleration Fully automated Work (electrical) [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA] 0502 economics and business Automotive Engineering Reinforcement learning State (computer science) ComputingMilieux_MISCELLANEOUS |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems IEEE Transactions on Intelligent Transportation Systems, IEEE, In press |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2021.3114983 |
| Popis: | High-speed highway on-ramp merging is a significant challenge toward realizing fully automated driving (level 4). Connected Autonomous Vehicles (CAVs), that combine communication and autonomous driving technologies, may improve greatly the safety performances when performing highway on-ramp merging. However, even with the emergence of CAVs, some keys constraints should be considered to achieve a safe on-ramp merging. First, human-driven vehicles will still be present on the road, and it may take decades before all the commercialized vehicles will be fully autonomous and connected. Also, onboard vehicle sensors may provide inaccurate or incomplete data due to sensors limitations and blind spots, especially in such critical situations. To resolve these issues, the present work introduces a novel solution that uses an off-board Road-Side Unit (RSU) to realize fully automated highway on-ramp merging for connected and automated vehicles. Our proposed approach is based on an Artificial Neural Network (ANN) to predict drivers' intentions. This prediction is used as an input state to a Deep Reinforcement Learning (DRL) agent that outputs the longitudinal acceleration for the merging vehicle. To achieve this, we first propose a data-driven model that can predict the behavior of the human-driven vehicles in the main highway lane, with 99% accuracy. We use the output of this model as input state to train a Twin Delayed Deep Deterministic Policy Gradients (TD3) agent that learns ``safe'' and ``cooperative'' driving policy to perform highway on-ramp merging. We show that our proposed decision-making strategy improves performance compared to the solutions proposed previously. |
| Databáze: | OpenAIRE |
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