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On Board Diagnostics norms enforced by regulatory authorities of many countries require logging of distance traveled by the vehicle with MIL (malfunction indicator lamp) illuminated. This log needs to be maintained in non-volatile ECU memory. Conventional techniques maintain the log in a volatile memory during vehicle run-time and transfer the same to non-volatile memory when ignition is turned off. This requires use of a “power-hold” relay to keep an ECU power alive while the logged data in volatile memory is being transferred to non-volatile memory when ignition is switched-off. A novel strategy described in this paper avoids interface with power-hold relay, thereby reducing the system complexity. The design philosophy described makes use of an EEPROM to maintain the distance log. An innovative algorithm is employed to ensure that endurance specifications are not violated during the vehicle life-time.

Tire inflation pressure has a significant impact over vehicle driving dynamics, fuel consumption as well as tire life. Therefore, continuous monitoring of tire pressure becomes imperative for ride comfort, safety and optimum vehicle handling performance. Two types of tire pressure monitoring systems (TPMS) used by vehicles are - direct and indirect TPMS. Direct systems deploy pressure sensors at each wheel and directly send pressure value to the vehicle Controller Area Network (CAN). Indirect sensors on the other hand use the information from already existing sensors and some physics-based equations to predict the value of tire pressure. Direct TPMS tend to be more accurate but have higher cost of installation while indirect TPMS comes with a minimum cost but compromised accuracy. A digital proof-of-concept study for indirect TPMS development of a non-ESP vehicle based on machine learning (ML) technique is elaborated in this paper.

With the revolutionary advancements in modern transportation, offering advanced connectivity, automation, and data-driven decision-making has put the intelligent transportation systems (ITS) to a high risk from being exposed to cyber threats. Development of modern transportation infrastructure, connected vehicle technology and its dependency over the cloud with an aim to enhance safety, efficiency, reliability and sustainability of ITS comes with a lot more opportunities to protect the system from black hats. This paper explores the landscape of cyber threats targeting ITS, focusing on their potential impacts, vulnerabilities, and mitigation strategies. The cyber-attacks in ITS are not just limited to Unauthorized Access, Malware and Ransomware Attacks, Data Breaches, Denial of Service but also to Physical Infrastructure Attacks.

This literature is in the field of communication networks where different Electronic Control Units (ECUs) communicate with each other over Controller Area Network (CAN) protocol. Typically these types of CAN networks are widely used in automotive vehicles, plant automations, etc. This proposed method is applicable in all such applications where controller area network is used as backbone electrical architecture. This literature proposes a new method of CAN signal packing into CAN frames so that network bus-load is minimized so that more number of CAN signals can be packed and more number of ECUs can be accommodated within a CAN network. The proposed method also ensures that the age of each CAN signal is minimized and all CAN signals reach the intended receiving ECUs within their maximum allowed age. Typically network designers are forced to design and develop multiple sub-networks and network gateways to get rid of network bus-load.

Increasing number of ECU's (Electronic Control Units) being used in modern vehicles have given rise to HIL (hardware in the loop) testing, and model based design approach to design/test ECU's even before the proto-type vehicle is ready. However, it is not uncommon to discover surprising system design lapses during actual vehicle operation reported after vehicle launch. Major cause behind such lapses are found to be the gap between actual field performance/robustness of various vehicle sub-systems interfaced with ECU's and those modeled as ideal cases during HIL testing in the lab. This creates a need to evolve effective vehicle-level validation strategies to expose such performance deficiencies of real life sub-systems provided by the vendors. This paper describes a new approach to validate ECU in real time.

This paper is in the field of communication networks where different Electronic Control Units (ECUs) communicate with each other using various communication protocols such as Controller Area Network (CAN), Local Interconnect Network (LIN), FlexRay, etc. Typically such types of communication networks are widely used in automobile domain. This paper proposes a holistic approach for evaluation and finalization of complex network architecture for a vehicle. As part of this proposed method, at first one reference-network architecture is constructed for the highest end variant of the vehicle considering all possible ECUs for this vehicle. Then other possible logical variations in network architecture are constructed, which can also technically represent the vehicle's network architecture. Then a set of criteria measures are considered to evaluate how good (or bad) each variation of network architecture is with respect to the reference-network architecture.

As the vehicle functions are getting distributed over multiple ECUs in order to realize various complex control functions, the need for sophisticated on-board diagnostic strategies are increasing in automotive domain, leading to a significant amount of hardware and software implementations for fault management inside various ECUs in the vehicle. This paper proposes optimized vehicle level approach for fault management strategies, wherein a centralized intelligent Gateway Module is proposed in the vehicle network architecture, which will be responsible for fault management of the complete vehicle in a chronological sequence. This Gateway Module will thereby have the possibility to group a cluster of faults raised by different ECUs and correlate them meaningfully to guide the operator towards root cause of the fault.