Performance Evaluation of Zone-Based In-Vehicle Network Architecture for Autonomous Vehicles

Park, Chulsun; Park, Sungkwon

doi:10.3390/s23020669

Cited by 19 publications

(6 citation statements)

References 15 publications

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“…This is an important factor that can affect data transfer time, wiring harnesses, and architectural complexity. In previous research [9], we verified that the ZIA outperforms the DIA in data transmission and wiring harnesses aspects through simulations. However, since we designed the ZIA with six zones and conducted simulations, verification of how the number of zones affects it has yet to be performed.…”

Section: Introductionsupporting

confidence: 61%

Analysis of E2E Delay and Wiring Harness in In-Vehicle Network with Zonal Architecture

Park,

Cui,

Park

2024

Sensors

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With recent advances in vehicle technologies, in-vehicle networks (IVNs) and wiring harnesses are becoming increasingly complex. To solve these challenges, the automotive industry has adopted a new zonal-based IVN architecture (ZIA) that connects electronic control units (ECUs) according to their physical locations. In this paper, we evaluate how the number of zones in the ZIA affects the end-to-end (E2E) delay and the characteristics of the wiring harnesses. We evaluate the impact of the number of zones on E2E delay through the OMNeT++ network simulator. In addition, we theoretically predict and analyze the impact of the number of zones on the wiring harnesses. Specifically, we use an asymptotic approach to analyze the total length and weight evolution of the wiring harnesses in ZIAs with 2, 4, 6, 8, and 10 zones by incrementally increasing the number of ECUs. We find that as the number of zones increases, the E2E delay increases, but the total length and weight of the wiring harnesses decreases. These results confirm that the ZIA effectively uses the wiring harnesses and mitigates network complexity within the vehicle.

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Section: Introductionsupporting

confidence: 61%

Analysis of E2E Delay and Wiring Harness in In-Vehicle Network with Zonal Architecture

Park,

Cui,

Park

2024

Sensors

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“…The restriction to one spatial direction in humanoid robots leads to a higher percentage of weight and length savings, since practically only the connections to the furthest away components and short branches to the closer components are determined. Park reports reduction in length of 24.7% and in mass of 24.6% [6]. The pure values of Park seem superior, while one needs to consider that he focuses on end-to-end timing of messages and does not provide information regarding the design procedure of the wiring harness as well as the distribution of the ECUs in the vehicle.…”

Section: Discussionmentioning

confidence: 99%

“…Autonomous driving, digitalization, and electric mobility place new demands on vehicles and their systems such as the introduction of voltages above 60 V, the integration of additional sensors (lidar, radar) and data processing units for autonomous driving (computer vision, artificial intelligence), cloud services, and better connectivity within the vehicle and with its environment [6]. With a rising number of functions, the complexity of the vehicle increases, thus the in-vehicle networks need to fulfill higher and completely new requirements, which makes a profound change indispensable.…”

Section: Introductionmentioning

confidence: 99%

Design of Zonal E/E Architectures in Vehicles Using a Coupled Approach of k-Means Clustering and Dijkstra’s Algorithm

Maier,

Reuss

2023

Energies

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Electromobility and autonomous driving has started a transformation in the automotive industry, resulting in new requirements for vehicle systems. Due to its functions, the electrical/electronic (E/E) architecture is one of the essential systems. Zonal E/E architecture is a promising approach to tackle this issue. The research presented in this paper describes a methodology for determining the optimal number of zones, the position of the zone control units (ZCU), and the assignment of electric components to these zones and ZCUs. Therefore, the design of the power supply and the wiring harness is essential. This approach aims to identify the most suitable system architecture for a given vehicle geometry and a set of electric components. For this purpose, the assignment of electric components is accomplished by k-means clustering, and Dijkstra’s algorithm is used to optimize the cable routing. As ZCUs will be the hubs for the in-vehicle data and information transport in zonal architectures, their position and their number are crucial for the architecture and wiring harness development. Simulations show a suitable zonal architecture reduces wiring harness length as well as weight and brings functional benefits. However, the number of zones must be chosen with care, as there may also be functional limitations.

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“…Similarity learning finds and matches similar images as the user-input keywords [41][42][43]. The proposed intelligent system matches the user-input keywords with the outer environment textual cues.…”

Section: Similarity Learningmentioning

confidence: 99%

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

Unar

Liu

et al. 2023

Sensors

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The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.

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Performance Evaluation of Zone-Based In-Vehicle Network Architecture for Autonomous Vehicles

Cited by 19 publications

References 15 publications

Analysis of E2E Delay and Wiring Harness in In-Vehicle Network with Zonal Architecture

Analysis of E2E Delay and Wiring Harness in In-Vehicle Network with Zonal Architecture

Design of Zonal E/E Architectures in Vehicles Using a Coupled Approach of k-Means Clustering and Dijkstra’s Algorithm

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

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