<div class="section abstract"><div class="htmlview paragraph">Vehicular odometers serve as a standard component in driver assistance system to provide continuous navigation. Odometer fraud is the disconnection, resetting, or alteration of a vehicle’s odometer with the intent to change the number of miles indicated. Odometer fraud occurs when the seller of a vehicle falsely represents the actual mileage of a vehicle to the buyer. But the Odometer readings are essential when it comes to ascertaining the fair market value of a used vehicle. Hence, there is a need to protect the odometer which resides in the instrument cluster of the digital cockpit. Any manipulation is very difficult to detect and to prove once made, even by expert technicians using specific On-Board Diagnostics (OBD) testing devices. One of the most critical issues is that currently odometers are not locked out from external access, in contrast to other vehicle components, which have higher protection levels. As a result, odometers are not sufficiently cyber-secured and there is a need to identify one – or more – technical solutions that would prevent their tampering. In this regard, a secure odometer solution is proposed here, which is deployed on a tamper proof hardware module with security features that are not available in the core processor in any Electronic Control Units (ECUs) to execute the proposed secure odometer solution. The secure tamper proof hardware is an add-on module to any vehicular processors for providing secure environment compared to conventional Hardware Security Modules (HSMs). The secure storage is stored a zero miles/kms value during the manufacturing by provisioning either in the trusted execution environment or the secure tamper proof hardware. The calculated distance from the odometer is added cumulatively into the secure storage distance under a fixed certain frequency. During each vehicle boot, the odometer error is calculated between the displayed distance and the distance stored in the secure storage like Replay Protected Memory Block (RPMB). If the error exceeds the fixed threshold, then displayed distance is updated with the previously stored secure storage distance as a correction mechanism. Hence, odometer corruption is prevented.</div></div>
<div class="section abstract"><div class="htmlview paragraph">Automotive system functionalities spread over a wide range of sub-domains ranging from non-driving related components to complex autonomous driving related components. The requirements to design and develop these components span across software, hardware, firmware, etc. elements. The successful development of these components to achieve the needs from the stockholders requires accurate understanding and traceability of the requirements of these component systems. The high-level customer requirements transformation into low level granularity requires an efficient requirement engineer. The manual understanding of the customer requirements from the requirement documents are influenced by the context and the knowledge gap of the requirement engineer in understanding and transforming the requirements. The manual way of understanding the requirements of the automotive systems always involve a certain amount of ambiguity, misunderstanding, bias etc. in analyzing the functionality of the requirements. The complex automotive system, which is solely developed based on human understanding always causes some violations in transforming the actual requirements from the stockholders in a product functionality. Hence, to mitigate this human influence on this aspect of requirement understanding, an intelligent system, which either to assists the manual requirement analysis or completely analyze the requirements alone is required. In this regard, an intelligent system is proposed here to analyze the automotive requirements efficiently by reducing human conflict, manual efforts, and to improve design and execution performance of an automotive component. The proposed system uses deep learning based Natural Language Processing (NLP) based algorithms to analyze and understand the requirement corpus from a set of platform requirements. The training of the deep learning CNN algorithms is performed on a huge set of pre-implemented platform requirements. The inference of the new customer requirements is done using the trained deep learning-based models to classify the requirements into one of the pre-defined platform requirement classes, thereby assisting the manual analysis using its intelligent component by also providing the traceability.</div></div>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.