Connected Vehicles: A Privacy Analysis

• URL: http://arxiv.org/abs/2207.06182v1
• Date: Wed, 13 Jul 2022 13:26:12 GMT
• Title: Connected Vehicles: A Privacy Analysis
• Authors: Mark Quinlan, Jun Zhao, Andrew Simpson
• Abstract summary: A modern car is capable of processing, analysing and transmitting data in ways that could not have been foreseen only a few years ago. We examine the telematics system of a production vehicle, and aim to ascertain some of the associated privacy-related threats.
• Score: 8.513938423514636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Just as the world of consumer devices was forever changed by the introduction of computer controlled solutions, the introduction of the engine control unit (ECU) gave rise to the automobile's transformation from a transportation product to a technology platform. A modern car is capable of processing, analysing and transmitting data in ways that could not have been foreseen only a few years ago. These cars often incorporate telematics systems, which are used to provide navigation and internet connectivity over cellular networks, as well as data-recording devices for insurance and product development purposes. We examine the telematics system of a production vehicle, and aim to ascertain some of the associated privacy-related threats. We also consider how this analysis might underpin further research.

Related papers

• Analyzing Privacy Implications of Data Collection in Android Automotive OS [15.970539534298137]
  Android Automotive OS (AAOS) has been integrated into over 100M vehicles. With this extensive data collection, privacy has become increasingly crucial. This paper focuses on the privacy implications of AAOS, examining the exact nature and scope of data collection and the corresponding privacy policies from the original equipment manufacturers (OEMs)
  arXiv  Detail & Related papers  (2024-09-23T21:35:40Z)
• Tapping in a Remote Vehicle's onboard LLM to Complement the Ego Vehicle's Field-of-View [1.701722696403793]
  We propose a concept to complement the ego vehicle's field-of-view (FOV) with another vehicle's FOV by tapping into their onboard language models (LLMs) Our results show that very recent versions of LLMs, such as GPT-4V and GPT-4o, understand a traffic situation to an impressive level of detail, and hence, they can be used even to spot traffic participants.
  arXiv  Detail & Related papers  (2024-08-20T12:38:34Z)
• Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis [6.422370188350147]
  We present a framework that monitors active vehicles using camera images and state information in order to determine whether vehicles are autonomous. Essentially, it builds on the cooperation among vehicles, which share their data acquired on the road feeding a machine learning model to identify autonomous cars. Experiments show it is possible to discriminate the two behaviors by analyzing video clips with an accuracy of 80%, which improves up to 93% when the target state information is available.
  arXiv  Detail & Related papers  (2024-03-14T17:00:29Z)
• Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions [2.693342141713236]
  This paper reviews publications on computer vision and autonomous driving that are published during the last ten years. In particular, we first investigate the development of autonomous driving systems and summarize these systems that are developed by the major automotive manufacturers from different countries. Then, a comprehensive overview of computer vision applications for autonomous driving such as depth estimation, object detection, lane detection, and traffic sign recognition are discussed.
  arXiv  Detail & Related papers  (2023-11-15T16:41:18Z)
• LLM4Drive: A Survey of Large Language Models for Autonomous Driving [62.10344445241105]
  Large language models (LLMs) have demonstrated abilities including understanding context, logical reasoning, and generating answers. In this paper, we systematically review a research line about textitLarge Language Models for Autonomous Driving (LLM4AD).
  arXiv  Detail & Related papers  (2023-11-02T07:23:33Z)
• Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward [0.0]
  This chapter explores the complex realm of autonomous cars, analyzing their fundamental components and operational characteristics. The primary focus of this investigation lies in the realm of cybersecurity, specifically in the context of autonomous vehicles. A comprehensive analysis will be conducted to explore various risk management solutions aimed at protecting these vehicles from potential threats.
  arXiv  Detail & Related papers  (2023-09-25T15:19:09Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies [56.77079930521082]
  We have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage.
  arXiv  Detail & Related papers  (2022-12-20T15:26:39Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.