Challenges in automotive cyber-physical systems design

Goswami, Dip; Schneider, Reinhard; Masrur, Alejandro; Lukasiewycz, Martin; Chakraborty, Samarjit; Voit, Harald; Annaswamy, Anuradha M.

doi:10.1109/samos.2012.6404199

Cited by 47 publications

(13 citation statements)

References 11 publications

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“…Autonomous driving is a typical cyber-physical system [236], where the computing systems and the physical world have to work closely and smoothly. With a human driver, the feeling of a driver is easily coupled with the vehicle control actions.…”

Section: K Physical Worlds Couplingmentioning

confidence: 99%

Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

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The recent proliferation of computing technologies, e.g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e.g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors. The key to the success of these autonomous systems is making a reliable decision in a real-time fashion. However, accidents and fatalities caused by early deployed autonomous vehicles arise from time to time. The real traffic environment is too complicated for the current autonomous driving computing systems to understand and handle. In this paper, we present the state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by eleven challenges and opportunities to realize autonomous driving. We hope this paper will gain attention from both the computing and automotive communities and inspire more research in this direction.

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Section: K Physical Worlds Couplingmentioning

confidence: 99%

Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

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“…While the distributed and heterogeneous nature of automotive E/E architectures makes them a perfect candidate for CPS-oriented design, it is only recently that some progress has been made in this direction [6], [7].…”

Section: Cps-oriented Designmentioning

confidence: 99%

Automotive Cyber–Physical Systems: A Tutorial Introduction

et al. 2016

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“…In such systems, scheduling configuration should consider constraints of all communication networks and system dynamics to properly introduce the commence time for each action and message assignment to time slots. A desirable scheduling framework utilizes combinations of computational solvers to reach an efficient and modular configuration for the entire system [44].…”

Section: Wireless Communication Packet-lossmentioning

confidence: 99%

A Wireless-Communication-Based Active Safety System for Articulated Heavy Vehicles

Mirfakhraie

Liscano

Zhu

et al. 2015

Volume 12: Transportation Systems

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This paper presents an active trailer steering (ATS) system using a wireless communication link to facilitate exchanging information among vehicle units of articulated heavy vehicles (AHVs). A challenge for developing and implementing the wireless-communication-based ATS system is to address the problems of delay and packet-loss. Embedding wireless communication transceivers on an AHV may introduce delays for data exchange, and the transmitted data may be lost. Many reasons may lead to the delay and packet-loss, e.g., channel fading, noise burst, interference, etc. The proposed ATS system may prevent unstable motion modes of AHVs if the sensor data reach the controllers/actuators in real-time with an acceptable level of delay and packet-loss. In order to ensure the performance of the ATS control, a Kalman-filter-based estimator is introduced. The estimator uses the available dynamic data to estimate the current states of the AHV in case some sensor information is not available due to a delay or an outage in the wireless communication link. To investigate the effect of the time delay due to the wireless communication on the performance of the ATS control, co-simulations are conducted. The wireless network is modeled using TrueTime toolbox, the ATS controller is designed in SimuLink package, and the AHV model is constructed in TruckSim software. Integrating the wireless network modeled in TrueTime, the ATS controller designed in SimuLink, and the AHV model constructed in TruckSim leads to the co-simulation platform. Under the emulated double lane-change test maneuver, the effects of the wireless communication with two schemes on the direction performance of the AHV are examined.

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Challenges in automotive cyber-physical systems design

Cited by 47 publications

References 11 publications

Edge Computing for Autonomous Driving: Opportunities and Challenges

Edge Computing for Autonomous Driving: Opportunities and Challenges

Automotive Cyber–Physical Systems: A Tutorial Introduction

A Wireless-Communication-Based Active Safety System for Articulated Heavy Vehicles

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