Software Platform for Heterogeneous In-Vehicle Environments

Milosevic, Milena; Bjelica, Milan Z.; Maruna, Tomislav; Teslić, Nikola

doi:10.1109/tce.2018.2844737

Cited by 18 publications

(5 citation statements)

References 23 publications

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“…The system provides an emergency notification function for high-speed head-on and single-vehicle accident victims that can summon rescue services within the golden window. The proposed IoV system is based on an already developed, next-generation hybrid IoV system [28] that successfully combines various in-vehicle (IV) networks [3], [29] (including a controller area network (CAN) bus, Bluetooth (BT), Bluetooth low energy (BLE), and power line communication (PLC) networks [30]), a vehicle-to-vehicle (V2V) network (visible light communication (VLC) [31]), and vehicle-toinfrastructure (V2I) networks (such as 3G/4G mobile networks and Wi-Fi networks). These heterogeneous networks can be interconnected via an in-vehicle infotainment (IVI) telematics platform [23].…”

Section: The Proposed Deepcrash Iov Systemmentioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

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Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergencyrelated announcements is approximately 7 s. INDEX TERMS Advanced driver assistance system (ADAS), artificial intelligence over Internet of Things (AIoT), automotive, deep learning, Internet of Vehicles (IoV), head-on and single-vehicle accident detection.

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Section: The Proposed Deepcrash Iov Systemmentioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

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“…The test driver determines the inputs of the SUT on driving scenarios and observes the reaction of the car, supported by special diagnosis and measurement hardware/software that records the data during the test drive and allows the behavior to be analyzed offline. An appropriate test platform must be chosen depending on the test object, purpose, or environment [28], [37].…”

Section: B Test Platformsmentioning

confidence: 99%

FAT-AES: Systematic Methodology of Functional Testing for Automotive Embedded Software

et al. 2022

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Road vehicles have incorporated several functionalities over the last decade, with an increasing incorporation of electronic embedded systems. Most of those functionalities are controlled, managed, and supervised by distributed software, within many interconnected Electronic Control Units (ECUs). Within such context, new methodologies for tests of the distributed software functions must be developed to ensure proper performance at the integration level, complying with the requirement specifications. Many strategies have emerged to organize the multiple levels of software testing in automotive embedded systems, to reduce costs and improve its effectiveness and robustness, but there is a need for a methodology to structure, optimize, and plan the tests with real automotive criteria. This work aims to extend the multiple levels of testing concept and propose a method to analyze, design, and evaluate the application system upfront to derive a software testing plan. This method has been developed incorporating the automotive application characteristics, including functional safety requirements specified in the ISO 26262 standard to structure and plan the embedded software test in vehicle development. The proposed method combines an enhancement on plan testing strategy, matching requirement specifications, with adherence to the current methods used in practice by the automotive industry. Such a process will help the automotive industry to follow some concrete steps during validation, to optimize the test volume and facilitate documentation of developed activities, improving the safety and security of automotive systems in the early stages of automotive embedded software, when the details of each function are not yet implemented at the component level.

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“…The thread-level performance is measured by deriving the relation between threads and throughput and between threads and latency from Equations ( 12), ( 14) and (15).…”

Section: Performance Measuresmentioning

confidence: 99%

“…Recently, the in-vehicle communication device has been evolving as an integrated device. In the in-vehicle communication, there is a problem that the complexity of the electronic control units (ECU) increases to about 150 per vehicle depending on the type and level of advanced driver assistance systems technology, so the need for an integrated ECU is emerging [15][16][17][18][19][20]. Even in V2X communication, if different devices are composed according to the service application, the physical configuration and interworking can be complicated.…”

Section: Introductionmentioning

confidence: 99%

Thread-Based Modeling and Analysis in Multi-Core-Based V2X Communication Device

Choi

2022

Sustainability

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We propose a thread-based modeling and analysis method for a vehicle-to-everything (V2X) communication device, according to the performance requirements of the V2X application. For a developer to develop a V2X device or application, its performance requirements must first be determined. Furthermore, the developer selects the hardware system to enable the service to function while satisfying the performance requirements. Through this process, the developer designs and creates the program in a manner that considers thread configuration and core mapping with the given hardware resources. Then, the performance evaluation of a system is tested via instruction-level program analysis with executable programs. This process is an essential procedure for developing a program that satisfies the performance requirements. However, the debugging procedure repeated through program development, performance analysis, and program modification requires significant time and is costly. Hence, to reduce the time and cost of unnecessary work, we propose a thread-level modeling and analysis method using a queueing theory for a V2X communication device that can be applied at the design level. First, we propose a thread-level performance modeling and analysis method based on queuing theory in a multi-core-based vehicle service system. Furthermore, we analyze the performance of a multi-core-based vehicle service system utilizing the proposed method.

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Software Platform for Heterogeneous In-Vehicle Environments

Cited by 18 publications

References 23 publications

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

FAT-AES: Systematic Methodology of Functional Testing for Automotive Embedded Software

Thread-Based Modeling and Analysis in Multi-Core-Based V2X Communication Device

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