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This paper focuses on the detection of cyber-attack on a communication channel and simultaneous radar health monitoring for a connected vehicle. A semi-autonomous adaptive cruise control (SA-ACC) vehicle is considered which has wireless communication with its immediately preceding vehicle to operate at small time-gap distances without creating string instability. However, the reliability of the wireless connectivity is critical for ensuring safe vehicle operation. The presence of two unknown inputs related to both sensor failure and cyber-attack seemingly poses a difficult estimation challenge. The dynamic system is first represented in descriptor system form. An observer with estimation error dynamics decoupled from the cyber-attack signal is developed. The performance of the observer is extensively evaluated in simulations. The estimation system is able to detect either a fault in the velocity measurement radar channel or a cyber-attack. Also, the proposed observer-based controller achieves resilient SA-ACC system under the cyber-attacks. The fundamental estimation algorithm developed herein can be extended in the future to enable cyber-attack detection in more complex connected vehicle architectures.
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A Reliability-Aware Vehicular Crowdsensing System for Pothole Profiling
Accurately profiling potholes on road surfaces not only helps eliminate safety related concerns and improve commuting efficiency for drivers, but also reduces unnecessary maintenance cost for transportation agencies. In this paper, we propose a smartphone-based system that is capable of precisely estimating the length and depth of potholes, and introduce a holistic design on pothole data collection, profile aggregation and pothole warning and reporting. The proposed system relies on the built-in inertial sensors of vehicle-carried smartphones to estimate pothole profiles, and warn the driver about incoming potholes. Because of the difference in driving behaviors and vehicle suspension systems, a major challenge in building such system is how to aggregate conflicting sensory reports from multiple participating vehicles. To tackle this challenge, we propose a novel reliability-aware data aggregation algorithm called Reliability Adaptive Truth Discovery (RATD). It infers the reliability for each data source and aggregates pothole profiles in an unsupervised fashion. Our field test shows that the proposed system can effectively estimate pothole profiles, and the RATD algorithm significantly improves the profiling accuracy compared with popular data aggregation methods.
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- PAR ID:
- 10294399
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 3
- Issue:
- 4
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3369815
