Realistic Cooperative Perception for Connected and Automated Vehicles: A Simulation Review

in Hawlader, Faisal; Frank, Raphaël (Eds.) 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023, June 16)

The Ugly Truth of Realistic Perception in Vehicular Simulations

Poster (2023, January 08)

Automated vehicles use sensors to perceive the environment, and studies have shown the limitations of these sensors. The onboard sensors may not detect objects when other participants occlude the Field of ... [more ▼]

Automated vehicles use sensors to perceive the environment, and studies have shown the limitations of these sensors. The onboard sensors may not detect objects when other participants occlude the Field of View (FoV). Thus, sensor efficiency must be tested to ensure its reliability. Simulation is an excellent test option due to the complexity associated with practical experiments. However, emerging simulation frameworks still have various limitations, especially when it comes to large-scale evaluation. This work investigates realistic perception simulation options for autonomous vehicles. We report the perception accuracy for different traffic scenarios. [less ▲]

Towards a Framework to Evaluate Cooperative Perception for Connected Vehicles

in Proceedings of the 13th IEEE Vehicular Networking Conference 2021 (2021)

Over the past few years, Connected and Autonomous Vehicles (CAVs) have gained significant research attention. With the recent deployment of 5G networks in many metropolitan areas, new cooperative driving ... [more ▼]

Over the past few years, Connected and Autonomous Vehicles (CAVs) have gained significant research attention. With the recent deployment of 5G networks in many metropolitan areas, new cooperative driving concepts are emerging. One of those is cooperative perception, where vehicles exchange sensory information via a V2X network to maximize their awareness horizon without the need for additional and potentially expensive sensors. The idea is to distribute the processing of the sensor information to find the best trade-off between data transmission and processing time. To experimentally evaluate the performance of cooperative perception schemes is time-consuming and costly due to the expensive hardware it involves. To the best of our knowledge there is to date no open- source simulation framework that allows to transfer realistic sensor data between multiple simulated vehicles via a V2X network. In this work-in-progress paper, we address this issue by proposing an extension of the well-known CARLA open- source simulator for automated driving research. We implement a basic communication channel on top of the existing client-server architecture of CARLA and show how sensor information can be exchanged. Transferring realistic sensor information between multiple vehicles opens up a wide range of experiments to test and evaluate novel approaches to collaborative driving in simulation. [less ▲]

Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

in Hawlader, Faisal; Boualouache, Abdelwahab; Faye, Sébastien (Eds.) et al The 2021 IEEE International Conference on Communications (the 4th Workshop on 5G and Beyond Wireless Security) (2021, June)

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed ... [more ▼]

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed to detect these attacks and mitigate their impact. However, existing ML-based MDSs require numerous features, which increases the computational time needed to detect attacks. In this context, this paper introduces a novel ML-based MDS for the early detection of position falsification attacks. Based only on received positions, our system provides real-time and accurate predictions. Our system is intensively trained and tested using a publicly available data set, while its validation is done by simulation. Six conventional classification algorithms are applied to estimate and construct the best model based on supervised learning. The results show that the proposed system can detect position falsification attacks with almost 100% accuracy. [less ▲]