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COMVEC™ conference is the only North American event that addresses vehicles and equipment spanning on-highway, off-highway, agricultural, construction, industrial, military, and mining sectors.

The 2022 COMVEC™ technical program focused on the commercial vehicle industry's crucial topics such as electrification, connectivity, off-highway, cybersecurity, autonomous and more!

The 2022 COMVEC™ technical program focused on the commercial vehicle industry's crucial topics such as electrification, connectivity, off-highway, cybersecurity, autonomous and more!

Join other young professionals in the mobility industry at the COMVEC™ meeting.

Annual conference government policy, regulatory makers, automotive industry neutral forum discuss US government regulation, technology, customer acceptance future vehicle design. industry event safety, emission control, fuel efficiency, automated vehicles.

The 2022 COMVEC™ technical program focused on the commercial vehicle industry's crucial topics such as electrification, connectivity, off-highway, cybersecurity, autonomous and more!

It is essential to note that cybersecurity threats not only arise from inherent protocol defects but also consider software implementation vulnerabilities.

Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper outlines the strategies and challenges of implementing an automotive Zero Trust Architecture (ZTA) to secure intra-vehicle networks. ...This research successfully met the four requirements and demonstrated that using ZT principles in an on-vehicle network greatly improved the cybersecurity posture with manageable impact to system performance and deployment.

UNECE R155 explicitly references ISO/SAE 21434 and mandates a certified cybersecurity management system (CSMS) as a prerequisite for automotive manufacturers to achieve vehicle type approval and sell new vehicle types. ...However, the gap in the CSMS framework is a lack in a standardized system that provides guidance and common criteria for automakers to measure a vehicle’s level of compliance and compute a publicly accepted cybersecurity rating. To help establish increased consumer confidence, OEMs and smart mobility stakeholders could take additional proactive steps to ensure the safety and security of their products. ...This paper addresses the above requirement and discusses the cybersecurity rating framework (CSRF) that could establish a framework for rating vehicle cybersecurity by standardizing the measurement criteria, parameter vectors, process, and tools.

This paper gives a definition of the SDV concept, provides views from different aspects, discusses the progress in vehicle E/E architecture, especially zone-based architecture with centralized computation, and various technologies including High-Performance Computing (HPC) platform, standardized vehicle software architecture, advanced onboard communication, Over-The-Air (OTA) update, and cybersecurity etc. that collectively enable the realization of SDV.

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.

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.

Abstract The secure boot has successfully protected systems from executing untrusted software (SW), but low-power controllers lack sufficient time to check every memory cell while satisfying real-time functional safety requirements. Automotive controllers need to maintain security through multiple cycles of remote, unsupervised operation and safely reach a secure state when an anomaly is detected. To accelerate the boot time, we propose Sliced Secure Boot: build fingerprints by slicing orthogonally through memory blocks, protect each cell with a reusable fingerprint using a reproducible pattern with sufficient entropy, and randomly check one fingerprint pattern during boot. We do not claim that sampling offers equivalent protection to exhaustive checks but demonstrate that careful sampling can provide a sufficient level of detection while maintaining compatibility with both startup time and functional safety requirements.

This exercise confirms the necessity of a more restrictive cybersecurity posture in automotive peripherals with access to critical systems, in particular VDAs, and especially when such peripherals present a wireless interface.

But unfortunately, automotive cybersecurity researchers hardly produce a comprehensive detection method due to the confidential nature of Controller Area Network (CAN) DBC format files, which is a standard long maintained by car manufacturers.

The new generation vehicles these days are managed by networked controllers. A large portion of the networks is planned with more security which has recently roused researchers to exhibit various attacks against the system. This paper talks about the liabilities of the Controller Area Network (CAN) inside In-vehicle communication protocol and a few potentials that could take due advantage of it. Moreover, this paper presents a few security measures proposed in the present examination status to defeat the attacks. In any case, the fundamental objective of this paper is to feature a comprehensive methodology known as Intrusion Detection System (IDS), which has been a significant device in getting network data in systems over many years. To the best of our insight, there is no recorded writing on a through outline of IDS execution explicitly in the CAN transport network system.

Using a wireless medium for tractor-trailer communication will bring new cybersecurity challenges and requirements which requires new development and lifecycle considerations.

CAN bus network proved to be efficient and dynamic for small compact cars as well as heavy-duty vehicles (HDV). However, HDVs are more susceptible to malicious attacks due to lack of security in their intra-vehicle communication protocols. SAE proposed a new standard named J1939-91C for CAN-FD networks which provides methods for establishing trust and securing mutual messages with optional encryption. J1939-91C ensures message authenticity, integrity, and confidentiality by implementing complex cryptographic operations including hash functions and random key generation. In this paper, the three main phases of J1939-91C, i.e., Network Formation, Rekeying, and Message Exchange, are simulated and tested on Electronic Control Units (ECUs) supporting CAN-FD network. Numerous test vectors were generated and validated to support SAE J1939-91C. The mentioned vectors were produced by simulating different encryption and hashing algorithms with variable message and key lengths.