A Full-fledge Simulation Framework for the Assessment of Connected Cars

Selvaraj, Dinesh Cyril; Hegde, Shailesh; Chiasserini, Carla Fabiana; Amati, Nicola; Deflorio, Francesco Paolo; Zennaro, Giuliana

doi:10.1016/j.trpro.2021.01.037

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…In general, (D)RL frameworks employ recognized simulators to validate their agent's performance [29]. In our work, we employ the CoMoVe simulation framework [30]: a sophisticated validation tool that can realistically simulate both detailed vehicle dynamics and communication models.…”

Section: Related Workmentioning

confidence: 99%

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An ML-Aided Reinforcement Learning Approach for Challenging Vehicle Maneuvers

Selvaraj

Hegde

Amati

et al. 2023

IEEE Trans. Intell. Veh.

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The richness of information generated by today's vehicles fosters the development of data-driven decision-making models, with the additional capability to account for the context in which vehicles operate. In this work, we focus on Adaptive Cruise Control (ACC) in the case of such challenging vehicle maneuvers as cut-in and cut-out, and leverages Deep Reinforcement Learning (DRL) and vehicle connectivity to develop a data-driven cooperative ACC application. Our DRL framework accounts for all the relevant factors, namely, passengers' safety and comfort as well as efficient road capacity usage, and it properly weights them through a two-layer learning approach. We evaluate and compare the performance of the proposed scheme against existing alternatives through the CoMoVe framework, which realistically represents vehicle dynamics, communication and traffic. The results, obtained in different real-world scenarios, show that our solution provides excellent vehicle stability, passengers' comfort, and traffic efficiency, and highlight the crucial role that vehicle connectivity can play in ACC. Notably, our DRL scheme improves the road usage efficiency by being inside the desired range of headway in cut-out and cut-in scenarios for 69% and 78% (resp.) of the time, whereas alternatives respect the desired range only for 15% and 45% (resp.) of the time. We also validate the proposed solution through a hardware-in-the-loop implementation, and demonstrate that it achieves similar performance to that obtained through the CoMoVe framework.

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Section: Related Workmentioning

confidence: 99%

“…The CoMoVe framework [30], depicted in Fig. 3, combines widely used simulators in each domain (mobility, communication, and vehicle dynamics) and makes them to interact efficiently.…”

Section: Integrating the Drl Model In Comovementioning

confidence: 99%