Design of a Misbehavior Detection System for Objects Based Shared Perception V2X Applications

Ambrosin, Moreno; Yang, Lixin; Liu, Xiruo; Sastry, Manoj; Álvarez, Ignacio

doi:10.1109/itsc.2019.8917066

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…• Trust, Reliability, Latencies, and Uncertainties: Not all units have the same reliability. For example, static units potentially have lower uncertainties, while surrounding vehicles can be less precise or even malicious and not trustworthy [20]. In this study, we limit the analysis to the uncertainties and do not consider security issues.…”

Section: B Copemsmentioning

confidence: 99%

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Piazzoni

Cherian

Vijay

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment. We focus our analysis on the occlusion problem in the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded objects. Cooperative perception (CP) solutions based on Vehicle-to-Everything (V2X) communications aim to avoid such issues by cooperatively leveraging additional points of view for the world around the AV. This approach usually requires many sensors, mainly cameras and LiDARs, to be deployed simultaneously in the environment either as part of the road infrastructure or on other traffic vehicles. However, implementing a large number of sensor models in a virtual simulation pipeline is often prohibitively computationally expensive. Therefore, in this paper, we rely on extending Perception Error Models (PEMs) to efficiently implement such cooperative perception solutions along with the errors and uncertainties associated with them. We demonstrate the approach by comparing the safety achievable by an AV challenged with a traffic scenario where occlusion is the primary cause of a potential collision.

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Section: B Copemsmentioning

confidence: 99%

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Piazzoni

Cherian

Vijay

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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“…Plausibility checking as a mean to improve perception at different stages of the sensor fusion process is explored for example in [12], [13]. Use cases comprise the detection of implausible ghost vehicles [14] or misbehaviour in vehicle-to-vehicle communication [15]. In previous work, we have further harnessed plausibility checking to detect sensor faults in distributed sensor networks [16].…”

Section: Related Workmentioning

confidence: 99%

Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach

Buerkle¹,

Geißler²,

Paulitsch³

et al. 2021

Preprint

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While the most visible part of the safety verification process of automated vehicles concerns the planning and control system, it is often overlooked that safety of the latter crucially depends on the fault-tolerance of the preceding environment perception. Modern perception systems feature complex and often machine-learning-based components with various failure modes that can jeopardize the overall safety. At the same time, a verification by for example redundant execution is not always feasible due to resource constraints. In this paper, we address the need for feasible and efficient perception monitors and propose a lightweight approach that helps to protect the integrity of the perception system while keeping the additional compute overhead minimal. In contrast to existing solutions, the monitor is realized by a well-balanced combination of sensor checks -here using LiDAR information -and plausibility checks on the object motion history. It is designed to detect relevant errors in the distance and velocity of objects in the environment of the automated vehicle. In conjunction with an appropriate planning system, such a monitor can help to make safe automated driving feasible.

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“…The work in [ 33 , 34 ] analytically evaluates the enhancement of environmental perception for CVs at different CP service penetration rates and with different traffic densities. The authors of [ 35 ] discuss the security threats in CP and propose possible countermeasures in V2X network protocols, while the work in [ 36 ] focuses on using CP for detecting vehicle misbehaviour due to adversarial attacks in V2X communication. Most of the CP related use cases studied in the literature are safety related, including cooperative driving [ 5 , 16 ], cooperative advisory warnings [ 37 , 38 ], cooperative collision avoidance [ 4 , 13 , 39 ], intersection assistance [ 17 , 40 ], and vehicle misbehaviour detection [ 41 ], to name a few.…”

Section: Related Workmentioning

confidence: 99%

Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations

Shan

Narula

Wong³

et al. 2020

Sensors

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Cooperative perception, or collective perception (CP), is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). The main contribution of the work lies in investigating and demonstrating the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in experiments that a connected vehicle (CV) can “see” a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision-making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.

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Design of a Misbehavior Detection System for Objects Based Shared Perception V2X Applications

Cited by 14 publications

References 18 publications

CoPEM: Cooperative Perception Error Models for Autonomous Driving

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach

Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations

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