Smart mobility of the future – a challenge for embedded automotive systems

Baunach, Marcel Carsten; Gomes, Renata Martins; Malenko, Maja; Mauroner, Fabian; Ribeiro, Leandro Batista; Scheipel, Tobias Peter

doi:10.1007/s00502-018-0623-6

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…For this reason, the MPC presented in this work has been tested on a real MCU, not based on FPGA but on a high-performance Arm-Cortex M7 MCU. This kind of MCUs will probably drive the future of the mobility [18,19], due to their low costs and good performance. In this work, the development board NUCLEO-H743ZI equipped with a STM32H743ZI MCU [20] has been used, due to the possibility provided by ST Microelectronics to enable PIL Testing [21] in the MATLAB/Simulink environment, which is the main software development tool for Model-Based-Design in the automotive field.…”

Section: Mpc For Cavsmentioning

confidence: 99%

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Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

Landolfi

Minervini²,

Minervini³

et al. 2021

WEVJ

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In the years to come, Connected and Automated Vehicles (CAVs) are expected to substantially improve the road safety and environmental impact of the road transport sector. The information from the sensors installed on the vehicle has to be properly integrated with data shared by the road infrastructure (smart road) to realize vehicle control, which preserves traffic safety and fuel/energy efficiency. In this context, the present work proposes a real-time implementation of a control strategy able to handle simultaneously motion and hybrid powertrain controls. This strategy features a cascade of two modules, which were implemented through the model-based design approach in MATLAB/Simulink. The first module is a Model Predictive Control (MPC) suitable for any Hybrid Electric Vehicle (HEV) architecture, acting as a high-level controller featuring an intermediate layer between the vehicle powertrain and the smart road. The MPC handles both the lateral and longitudinal vehicle dynamics, acting on the wheel torque and steering angle at the wheels. It is based on a simplified, but complete ego-vehicle model, embedding multiple functionalities such as an adaptive cruise control, lane keeping system, and emergency electronic brake. The second module is a low-level Energy Management Strategy (EMS) of the powertrain realized by a novel and computationally light approach, which is based on the alternative vehicle driving by either a thermal engine or electric unit, named the Efficient Thermal Electric Skipping Strategy (ETESS). The MPC provides the ETESS with a torque request handled by the EMS module, aiming at minimizing the fuel consumption. The MPC and ETESS ran on the same Microcontroller Unit (MCU), and the methodology was verified and validated by processor-in-the-loop tests on the ST Microelectronics board NUCLEO-H743ZI2, simulating on a PC-host the smart road environment and a car-following scenario. From these tests, the ETESS resulted in being 15-times faster than than the well-assessed Equivalent Consumption Minimization Strategy (ECMS). Furthermore, the execution time of both the ETESS and MPC was lower than the typical CAN cycle time for the torque request and steering angle (10 ms). Thus, the obtained result can pave the way to the implementation of additional real-time control strategies, including decision-making and motion-planning modules (such as path-planning algorithms and eco-driving strategies).

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Section: Mpc For Cavsmentioning

confidence: 99%

“…In particular, in (18), a soft constraint on d r is imposed, to accept at most a term equal tod sv (1 m in the case studies) below the theoretical safety distance d sa f e . Furthermore, the upper bound of 100 m is selected to keep the distance between the two vehicles limited.…”

mentioning

confidence: 99%

Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

Landolfi

Minervini²,

Minervini³

et al. 2021

WEVJ

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“…With the recent advances in information and communication technologies, mobility has an important role in providing a better quality of life. The Internet of Things integrates intelligence into existing research and development areas, such as smart-health, smart-home, smartenergy, smart industry and smart transport -or smart mobility as described in [29] (see Figure 3). The Internet of Vehicles (IoV) is one of the revolutions mobilized by IoT, which involves the concept of VANET -Vehicular Ad hoc Networks, to convey the vision of the smartphone to the smart car [30], [31].…”

Section: Internet Of Vehiclesmentioning

confidence: 99%

“…These systems should undergo a profound change from static and monolithic designs towards much more dynamic and compositional concepts with security, safety, real-time, and maintainability in mind, in order to be prepared for use cases with strict demands on dependability. The concepts must support advanced multicore and application-specific processor architectures, protection against environmental perturbation and attacks, dynamic update mechanisms, and simplified software portability for long-term operation [29]. The adoption of vehicular communication environments will include big data, security, privacy, reliability, mobility, and standards.…”

Section: A Technical Aspects Of Vehicular Communicationmentioning

confidence: 99%

Sustainable Adoption of Connected Vehicles in the Brazilian Landscape: Policies, Technical Specifications and Challenges

Nascimento

Iano

Loschi

et al. 2019

TEEE

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This review addresses the intervehicular communication in Connected Vehicles (CV) by emphasizing V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communications in terms of evolution, current standards, state-of-the-art studies, embedded devices, simulation, trends, challenges, and relevant legislation. This review is based on studies conducted from 2009 to 2019, government reports about the sustainable deployment of these technologies and their adoption in the Brazilian automotive market. Moreover, WAVE (Wireless Access in Vehicular Environment) and DSRC (Dedicated Short-range Communication) standards, the performance analysis of communication parameters and intervehicular available at the market are also described. The current status of ITS (Intelligent Transportation System) development in Brazil was reviewed, as well as the research institutes and governmental actions focused on introducing the concept of connected vehicles into the society. The Brazilian outlook for technological adoption concerning CVs was also discussed. Moreover, challenges related to technical aspects, safety and environmental issues, and the standardization for vehicle communication are also described. Finally, this review highlights the challenges and proposals from available technologies devoted to the roads and vehicular infrastructure communication, their evolution and upcoming trends.

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“…The automotive industry is constantly evolving, especially with the aim to increase user comfort and achieve autonomy, but keeping safety as a priority, which is being enabled by the development of computer science and electronic technologies [1]. In fact, most of the innovations in the industry are related to those two areas [2].…”

Section: Introductionmentioning

confidence: 99%

A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems

Delgadillo

Blieninger

Kuhn

et al. 2022

JLPEA

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Consolidating tasks to a smaller number of electronic control units (ECUs) is an important strategy for optimizing costs and resources in the automotive industry. In our research, we aim to enable ECU consolidation by migrating tasks at runtime between different ECUs, which adds redundancy and fail-safety capabilities to the system. In this paper, we present a setup with a generalistic and modular architecture that allows for integrating and testing different ECU architectures and machine learning (ML) models. As part of a holistic testbed, we introduce a collection of reproducible tasks, as well as a toolchain that controls the dynamic migration of tasks depending on ECU status and load. The migration is aided by the machine learning predictions on the schedulability analysis of possible future task distributions. To demonstrate the capabilities of the setup, we show its integration with FreeRTOS-based ECUs and two ML models—a long short-term memory (LSTM) network and a spiking neural network—along with a collection of tasks to distribute among the ECUs. Our approach shows a promising potential for machine-learning-based schedulability analysis and enables a comparison between different ML models.

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Smart mobility of the future – a challenge for embedded automotive systems

Cited by 5 publications

References 6 publications

Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

Sustainable Adoption of Connected Vehicles in the Brazilian Landscape: Policies, Technical Specifications and Challenges

A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems

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