Real-Time Hardware-In-the-Loop Emulation Framework for DSRC-based Connected Vehicle Applications
Not on the map. Why?
Abstract (arXiv)
The rapid growth of connected and automated vehicle (CAV) solutions have made a significant impact on the safety of intelligent transportation systems. However, similar to any other emerging technology, thorough testing and evaluation studies are of paramount importance for the effectiveness of these solutions. Due to the safety-critical nature of this problem, large-scale real-world field tests do not seem to be a feasible and practical option. Thus, employing simulation and emulation approaches are preferred in the development phase of the safety-related applications in CAVs. Such methodologies not only mitigate the high cost of deploying large number of real vehicles, but also enable researchers to exhaustively perform repeatable tests in various scenarios. Software simulation of very large-scale vehicular scenarios is mostly a time consuming task and as a matter of fact, any simulation environment would include abstractions in order to model the real-world system. In contrast to the simulation-based solutions, network emulators are able to produce more realistic test environments. In this work, we propose a high-fidelity hardware-in-the-loop network emulator framework in order to create testing environments for vehicle-to-vehicle (V2V) communication. The proposed architecture is able to run in real-time fashion in contrast to other existing systems, which can potentially boost the development and validation of V2V systems.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 4.
7 papers.
- 2021Social Coordination and Altruism in Autonomous Drivingcited by 28
- 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Trafficcited by 15
- 2021Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Criticcited by 11
- 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Drivingcited by 6
- 2021Towards Learning Generalizable Driving Policies from Restricted Latent Representationscited by 4
- 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed trafficcited by 1
- 2022Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systemscited by 0
- 2022Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems24 Dec 2022
- 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic22 Nov 2022
- 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving19 Nov 2022
- 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic2 Feb 2022
- 2021Towards Learning Generalizable Driving Policies from Restricted Latent Representations5 Nov 2021
- 2021Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic12 Jul 2021
- 2021Social Coordination and Altruism in Autonomous Driving1 Jul 2021