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Abstract “It cannot happen to us” is one of many common myths regarding cybersecurity in the transportation industry. The traditional view that the threats to transportation are low probability and low impact keep agencies from mitigating security threats to transportation critical infrastructure.

., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384.

The article also focus on innovative approaches that have recently adopted my many cybersecurity professionals for secured operation of ITS involving block-chain, artificial intelligence, and Machine Learning.

SAE is developing a number of standards, including the SAE J2945/x and SAE J3161/x series, that specify a set of applications using message sets from the SAE J2735 data dictionary. (“Application” is used here to mean “a collection of activities including interactions between different entities in the service of a collection of related goals and associated with a given IEEE Provider Service Identifier (PSID)”). Authenticity and integrity of the communications for these applications are ensured using digital signatures and IEEE 1609.2 digital certificates, which also indicate the permissions of the senders using Provider Service Identifiers (PSIDs) and Service Specific Permissions (SSPs). The PSID is a globally unique identifier associated with an application specification that unambiguously describes how to build interoperable instances of that application.

Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and onboard intelligence can may help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise the UGVs. In this paper, we consider a connected UGV platoon under cyber attacks that may disrupt safety and degrade performance. An observer-based resilient control strategy is designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. In addition, each UGV generates both internal and external evaluations based on the platoons performance metrics. A cloud-based trust-based information management system collects these evaluations to detect abnormal UGV platoon behaviors.

Abstract Connected vehicle technology consisting of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication falls under the umbrella of V2X, or Vehicle-to-Everything, communication. This enables vehicles and infrastructure to exchange safety-related information to enable smarter, safer roads. If driver alerts are raised or automated action is taken as a result of these messages, it is critical that messages are trustworthy and reliable. To this end, the Security Credential Management System (SCMS) and Cooperative Intelligent Transportation Systems (C-ITS) Credential Management System (CCMS) have been proposed to enable authentication and authorization of V2X messages without compromising individual user privacy. This is accomplished by issuing each vehicle a large set of “pseudonyms,” unrelated to any real-world identity. During operation, the vehicle periodically switches pseudonyms, thereby changing its identity to others in the network.

Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality.

Abstract Connected vehicles and intelligent transportation systems are currently evolving into highly interconnected digital environments. Due to the interconnectivity of different systems and complex communication flows, a joint risk analysis for combining safety and security from a system perspective does not yet exist. We introduce a novel method for joint risk assessment in the automotive sector as a combination of the Diamond Model, Failure Mode and Effects Analysis (FMEA), and Factor Analysis of Information Risk (FAIR). These methods have been sequentially composed, which results in a comprehensive risk management approach to information security in an intelligent transport system (ITS). The Diamond Model serves to identify and structurally describe threats and scenarios, the widely accepted FMEA provides threat analysis by identifying possible error combinations, and FAIR provides a quantitative estimation of probabilities for the frequency and magnitude of risk events.

Connected vehicles can provide data from multiple sensors that monitor both the vehicle and the environment through which the vehicle is passing. The data, when shared, can be used to enhance and optimize transportation operations and management—specifically, traffic flow and infrastructure maintenance. This document describes an interface between vehicle and infrastructure for collecting vehicle/probe data. That data may represent a single point in time or may be accumulated over defined periods of time or distance, or may be triggered based on circumstance. The purpose of this document is to define an interoperable means of collecting the vehicle/probe data in support of the use cases defined herein. There are many additional use cases that may be realized based on the interface defined in this document. Note that vehicle diagnostics are not included within the scope of this document, but diagnostics-related features may be added to probe data in a future supplemental document.

Editorial High noon for high-level autonomy The Navigator A fork in the road for the AV business The Electric, Autonomous Revolution Lifts Off Engineering the new generation of electric and hybrid vertical-take-off-and-landing vehicles at Wisk and Elroy Air. New SAE Standard for Automated-Driving Developers Developed in less than a year, SAE's new J3216 standard will impact traffic management, operations and safety for automated mobility. Making Data Logging, Replay and Prototyping More Efficient High levels of continuity and compatibility are vital to avoid interruptions in the development process - and reduce cost. Radar Death Star ELunewave's 3D-printed spherical antenna makes for fast, 360-degree single-snapshot readings that are claimed to beat the slower sweeps of conventional radar. The Case for FOTA in AV Data Security Firmware over-the-air data transmission helps OEMs drive secure vehicle autonomy.

Abstract With the adoption of Vehicle-to-everything (V2X) technology, security and privacy of vehicles are paramount. To avoid tracking while preserving vehicle/driver’s privacy, modern vehicular public key infrastructure provision vehicles with multiple short-term pseudonym certificates. However, provisioning a large number of pseudonym certificates can lead to an enormous growth of Certificate Revocation Lists (CRLs) during its revocation process. One possible approach to avoid such CRL growth is by relying on activation code (AC)-based solutions. In such solutions, the vehicles are provisioned with batches of encrypted certificates, which are decrypted periodically via the ACs (broadcasted by the back-end system). When the system detects a revoked vehicle, it simply does not broadcast the respective vehicle’s AC. As a result, revoked vehicles do not receive their respective AC and are prevented from decrypting their certificates.

Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.

This document applies to the development of Plans for integrating and managing COTS assemblies in electronic equipment and Systems for the commercial, military, and space markets; as well as other ADHP markets that wish to use this document. For purposes of this document, COTS assemblies are viewed as electronic assemblies such as printed wiring assemblies, relays, disk drives, LCD matrices, VME circuit cards, servers, printers, laptop computers, etc. There are many ways to categorize COTS assemblies1, including the following spectrum: At one end of the spectrum are COTS assemblies whose design, internal parts2, materials, configuration control, traceability, reliability, and qualification methods are at least partially controlled, or influenced, by ADHP customers (either individually or collectively). An example at this end of the spectrum is a VME circuit card assembly.

Abstract Vehicle to Everything (V2X) allows vehicles, pedestrians, and infrastructure to share information for the purpose of preventing accidents, enhancing road safety, and improving the efficiency and energy consumption of transportation. Although V2X messages are authenticated, their content is not validated. Sensor errors or adversarial attacks can cause messages to be perturbed and, therefore, increase the likelihood of traffic jams, compromise the decision process of other vehicles, or provoke fatal crashes. In this article, we introduce V2X Core Anomaly Detection System (VCADS), a system based on the theory presented in [1] and built for the fields provided in the periodic messages shared across vehicles (i.e., Basic Safety Messages, BSMs). VCADS uses physics-based models to constrain the values in each field and detect anomalies by finding the numerical difference between a field and independent derivations of the same field.

Abstract Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time.

It is deemed that currently the intelligent connected vehicle (ICV) is in its early stage of development, and it will go through multiple development stages in the future to realize its final goal—autonomous driving. Based on the existing ICV researches, this paper believes that ICV can be used to improve the efficiency and safety of freeway. The current research of ICV has two main directions: one focuses on the traffic flow characteristics of vehicles with different attributes, the other is concerned with using ICV to reduce congestion. From the policies issued by countries around the world and the development plans promoted by major vehicle manufacturers, the future development trends and challenges of ICV are analyzed. ICV must overcome all the shortcomings to achieve its final goal, including insufficient hardware capabilities or excessive cost, and the degree of intelligence that needs to be improved.

By looking into the vehicle-infrastructure cooperation (VIC) which is oriented towards intelligent, networked and integrated development, this paper analyzes and proposes the essence and development direction of Intelligent Vehicle Infrastructure Cooperation Systems (I-VICS). With an in-depth analysis of technologies of core importance to VIC and influence factors that constrain VIC development as a whole, the paper comes up with a technological route for VIC, and identifies a direction for vehicle-infrastructure cooperative development that progresses from primary to intermediate cooperation, then to advanced cooperation, and finally to full-fledged cooperation. Policy recommendations aiming at strengthening top-level design, building an integrated vehicle-infrastructure-cloud platform, expediting independence of key techs, building robust standards and regulations for VIC, enhancing workforce development as well as greater efforts at market promotion are put forward.

Abstract Vehicular communications face unique security issues in wireless communications. While new vehicles are equipped with a large set of communication technologies, product life cycles are long and software updates are not widespread. The result is a host of outdated and unpatched technologies being used on the street. This has especially severe security impacts because autonomous vehicles are pushing into the market, which will rely, at least partly, on the integrity of the provided information. We provide an overview of the currently deployed communication systems and their security weaknesses and features to collect and compare widely used security mechanisms. In this survey, we focus on technologies that work in an ad hoc manner. This includes Long-Term Evolution mode 4 (LTE-PC5), Wireless Access in Vehicular Environments (WAVE), Intelligent Transportation Systems at 5 Gigahertz (ITS-G5), and Bluetooth.

Abstract In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist.

Digitally enabled mobility vehicles and services, including dockless bikesharing and electric scooter sharing, are generating and collecting a growing amount of mobility data. Mobility data holds great potential to support transportation officials and their efforts to manage the public right-of-way, but the unlimited distribution of mobility data carries untested risks to privacy and public trust. The Mobility Data Collaborative™ has identified the need to improve and coordinate understanding among all parties around foundational policy and legal issues to support mobility data sharing, including privacy and contracting. The guidelines are geared towards supporting a scalable mobility data sharing framework that aligns the interests of the public and private sectors while addressing privacy, transparency, data ownership, and consumer trust.