Automotive Ecosystem Research Articles

Business ecosystem dynamics in the Chinese electric vehicle market: co-evolution of capabilities and dynamics

ABSTRACT Business Ecosystems (BEs) are, fundamentally, interaction systems of independent yet mutually dependent actors, but few studies have investigated the dynamic capabilities among the ecosystem stakeholders that drive the co-evolution process. To address this research gap, this paper explores the ecosystem stakeholder dynamics that influence firms in the automotive technology business ecosystem of Hefei, China. Guided by a proposed BE framework and using an exploratory abductive approach, qualitative data from semi-structured interviews were collected and analysed. After data collection and analysis of three Chinese electric vehicle case studies, the main findings of this paper are as follows. We introduce a Business Ecosystem Capabilities Framework (BECF) consisting of the Business Ecosystem Capabilities (BECs) at each phase of ecosystem development and the Business Ecosystem Dynamic Capabilities (BEDCs) that enable the ecosystem to transform to the next phase. Business Ecosystem Capabilities have four dimensions – Ecosystem Fertility, Ecosystem Enrichment, Ecosystem Strengthening, and Ecosystem Connectivity – with 12 subdimensions. The study provides valuable contributions to academics, consultants, and managers interested in BEs while also extending the literature on the emerging electric vehicle market currently disrupting the established automotive ecosystem.

Hybrid RNN-LSTM Networks for Enhanced Intrusion Detection in Vehicle CAN Systems

Electric vehicles (EVs) use electric motors for propulsion, relying on electric energy stored in batteries or other energy storage devices. The standard communication protocol used in EVs is the Control Area Network (CAN), a communication protocol widely used in the automotive industry for networking and communication between various components within a vehicle. CAN protocol, designed without any care about protection, as automotive systems become more connected, the vulnerability to cyber threats, including intrusion attacks. The most common intrusion attacks on EVs are Denial of Service (DoS), Fuzzy, and Impersonation Attacks. These become a significant challenge due to the imperative need for robust Intrusion Detection Systems (IDS) in CAN networks. This paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to enhance the effectiveness of intrusion detection in the EV domain. We will use a hybrid Deep-learning model to improve the analysis. First, we will apply the RNN model, and the output will come as input for the second model, LSTM. Our proposed hybrid model achieved an accuracy of 93%. The outcomes of this research contribute to advancing cybersecurity measures in vehicular networks, ensuring the integrity and safety of connected vehicles. The applicability of RNN and LSTM techniques in the context of CAN networks demonstrates their potential to evolve as integral components of next-generation intrusion detection systems, fostering a secure and resilient automotive ecosystem.

Exploring the Evolution of the Automotive Ecosystem in Morocco: Insights from Semi-Structured Interviews

Exploring the Evolution of the Automotive Ecosystem in Morocco: Insights from Semi-Structured Interviews

Exploring the evolution of the automotive ecosystem in Morocco: insights from semi-structured interviews

Exploring the evolution of the automotive ecosystem in Morocco: insights from semi-structured interviews

Omnichannel Strategy in Increasing Customer Satisfaction Post Covid-19 in Automotive Industry

COVID-19 has plagued the auto industry for the past two years. The automotive ecosystem is currently being hit by problems stemming from three main forces, namely competition, advances in digital technology, and increasingly advanced consumers. With so many choices on the market, it can be difficult to know where to start let alone make the right choice. The answer lies in the omnichannel experience. This study aims to highlight the critical factors for the successful incorporation of an omnichannel strategy. This research is a type of quantitative research to verify the theories that have been made through different tests and information handling. The population used in this research is Daihatsu car consumers at PT. Asco Prima Mobilindo East Java. The sampling technique used in this study was to use a probability sampling technique of 120. The results showed that the ability to provide services in the form of an integrated customer experience through different channels can have a positive impact on the level of car consumer satisfaction or it could be that the company is able to recognize , fulfilling and satisfying consumer needs very well so that consumer expectations and expectations can be achieved. The results of the two studies found that a natural, seamless, and sustainable cross-channel experience felt by car consumers also gave a very positive signal towards increasing the level of satisfaction felt by consumers.

Application of Federated Learning to Detect Malicious Behavior in Internet of Vehicles

With the escalating application of the Internet of Things (IoT) in several fields, implementation of IoT in the automotive ecosystem is currently one of the critical concerns due to the enormous potential for its expansion in unimaginable ways. Internet of Vehicles (IoV) applies to the present-day human-driven vehicles as well as impending autonomous ones. Smart transportation is significantly safer, cost-effective, more convenient, and more efficient. Despite offering plenty of benefits, IoV face serious issues including big data problems, user security and privacy, and vehicle reliability. Reliable connection channels are established but this doesn't eliminate the cyber risks associated with them. With the increasing frequency of these security incidents in IoV, guarding against these attacks has been the foremost priority. Regardless of the standard protocols and established frameworks, these attacks are still likely to endanger the vehicle and user privacy and security. To address the security and privacy issues, the primary focus of this paper is the application of federated learning to detect attacks on the security and privacy aspects of the IoV. Without using centralized data, the federated learning technique develops the prediction model utilizing user data from the devices. Thus, the model is collaborative and shared, and as model training comes down to devices, the user's data is secure as training data resides on the device and no specific versions are maintained in the cloud. Hence, the main objective is to employ a federated learning approach to ascertain any kind of malevolent conduct in the connected vehicle systems and propagate trusted, authentic and reliable information for better deployment.

Securing the quality capability of manufacturing processes – An approach to release high-level automated and chained manufacturing equipment

From the industrial revolution to the current transformation to industry 5.0, the emphasis is always on reducing production costs and increasing efficiency. The traditional lever of focusing on the automation of manufacturing processes is however becoming increasingly important in the automotive industry for many reasons. Industry-specific manufacturing challenges, such as fulfilling increased safety and quality requirements of self-driving and electric vehicles, are otherwise difficult to overcome. At the same time, the vehicle is transforming from a conventional car to an electric-driving automotive ecosystem. The required changes in the vehicle architecture are associated with the implementation of new technologies and lead to the consolidation of functions. In terms of electric vehicles, highly integrated, complex high-voltage components are the consequence. Subsequently, the manufacturing equipment for the production of these components is also becoming more complex, higher-level automated and need to be chained. The challenge that emerges from this development is the question how to check the quality capability of manufacturing processes and release them. In order to solve this issue, this paper presents a systematic approach for practitioners to release high-level automated and chained manufacturing equipment, taking into account their quality capability.

Enabling Far-Edge Intelligent Services with Network Applications: The Automotive Case

The fifth generation of mobile networks (5G) is rapidly reaching deployment across the globe, promising a series of advances for vertical service providers, both in terms of performance and in terms of operational capabilities. In this context, the 5G-IANA Network Application platform focuses on the rapidly advancing domain of intelligent, data centric, Artificial Intelligence/Machine Learning (AI/ML)-enabled applications, with a particular focus on the automotive domain. In this article, we present the key functional features designed for the support of such services including the integration of (mobile) far-edge resources, as well as ML-aware orchestration primitives. This includes novel features such as decision support for the optimal distribution of end-to-end ML pipelines, as well as run-time support for client selection in Federated Learning setups, far-edge failure handling and distribution drift aware lifecycle management. Such features come to address a series of limitations associated with legacy 5G management & orchestration systems, such as resource consumption of data centric services and privacy support. In this context, we further discuss the new opportunities arising for service provisioning and corresponding business models in the automotive ecosystem, with a particular emphasis on the implications of the emerging data and/or ML-model sharing schemes.

Enabling automated integration of architectural languages: An experience report from the automotive domain

Modern automotive software systems consist of hundreds of heterogeneous software applications, belonging to separated function domains and often developed within distributed automotive ecosystems consisting of original equipment manufactures, tier-1 and tier-2 companies. Hence, the development of modern automotive software systems is a formidable challenge. A well-known instrument for coping with the tremendous heterogeneity and complexity of modern automotive software systems is the use of architectural languages as a way of enabling different and specific views over these systems. However, the use of different architectural languages might come with the cost of reduced interoperability and automation as different languages might have weak to no integration. In this article, we tackle the challenge of integrating two architectural languages heavily used in the automotive domain for the design and timing analysis of automotive software systems: AMALTHEA and Rubus Component Model. The main contributions of this paper are (i) a mapping scheme for the translation of an AMALTHEA architecture into a Rubus Component Model architecture where high-precision timing analysis can be run, and the back annotation of the analysis results on the starting AMALTHEA architecture; (ii) the implementation of the proposed scheme, which uses the concept of model transformations for enabling a full-fledged automated integration; (iii) the application of such automation on three industrial automotive systems being the brake-by-wire, the full blown engine management system and the engine management system. We discuss and evaluate the proposed contributions using an online, experts survey and the above-mentioned use cases. Based on the evaluation results, we conclude that the proposed automation mechanism is correct and applicable in industrial contexts. Besides, we observe that the performance of the automation mechanism does not degrade when translating large models with several thousands of elements. Eventually, we conclude that experts in this field find the proposed contribution industrially relevant.

Private Vehicle Roadworthiness Inspection – Towards ELV Realization


 
 
 Being one of the most critical sectors worldwide, the rapid increase of automobility has necessitated effective management of End-of- Life Vehicles (ELVs). However, in Malaysia, an ELV policy and initiative is absent from the country's automotive ecosystem which resulted in a proliferation of aged and unsafe vehicles; thus potentially increasing the chance of occupant injury in a crash. The government had already mooted an ELV policy but due to public outcry, the proposed plan for the scrapping of aged vehicles was postponed. This paper aims to provide an overview of a potential private vehicle inspection to be introduced and also the challenges facing the government towards the implementation of the ELV directive in Malaysia. It is believed that the results presented in this paper will be useful to academics, government officials, and researchers to establish strategies for ELV policy in Malaysia effectively via a vehicle roadworthiness inspection.
 
 

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

Marketing technologies for the development of digital products in the automotive industry

The economy is changing dramatically under the influence of emerging markets, accelerated growth of new technologies, sustainable development policies and changing consumer preferences. New business models are emerging in many sectors of the economy and production. The automotive industry and the transport sector are no exception. New trends and a more personal approach to the consumer, digital products and services require a rethink of marketing activities in the automotive industry. Automakers are entering into partnerships with technology companies to increase customer loyalty and expand their list of familiar scenarios and ways of using transport.A mobility ecosystem is being formed all over the world. And this ecosystem is more than just “Automotive Industry 2.0”. To become a leader in the new landscape, you will need to fight with a large number of new players, many of whom do not have the traditional automotive experience and integration of various capabilities. It will be important for traditional automakers and suppliers, as well as new entrants, to adopt an ecosystem mindset.As digital technologies change the traditional boundaries of the industry, many are betting that the automotive ecosystem will be one of the first to develop. But what this will look like in practice, and when a drastic shift in competition will actually, occur can only be cautiously predicted so far.

An empirical study of vehicle to infrastructure communications - An intense learning of smart infrastructure for safety and mobility

Vehicle-to-Infrastructure (V2I) communication is a communication framework for wireless sharing of data between vehicles. V2I connectivity is usually used in wireless technology as a bi-directional communication through a system of hardware, software and firmware to support systems like lane signs, road signs and lighting systems. V2I provides a broad range of protection, mobility and environmental benefits by collecting and sharing so much of the data. Automobile vehicles that have long been a dream come true. It's time to build an effective and safe autonomous vehicle that allows everyone to take part in this market. In order to make this revolution possible, the automotive ecosystem also needs to change. This will improve transport safety and quality and will offer infotainment in new flexible systems. In this chapter, huge potential efforts are made to study the complete intensive insights of V2I with the detail of how V2I made a contribution to communication. In the end, this chapter also highlights applications for V2I-driven wireless technology. The performance of the V2I involved in the study were evaluated by using different measures related to running time, data rate and resource management.

Key Enablers for the Evolution of Aerospace Ecosystems

The aerospace industry is experiencing an unprecedented scenario. The air travel drifted from years of constant growth and positive expectations to a place where the uncertainty is the most predominant distinctive. Consequently, the aerospace ecosystem needs to adapt to cope with challenges never faced before. Understanding the evolution of the aerospace ecosystem is thus essential to foster its progression. This research aims at the identification and categorisation of key enablers that have been linked to the growth of aerospace ecosystems. To this extent, key enablers are first identified and then categorised using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) methodologies. An analysis is elaborated for a developed aerospace ecosystem, the United Kingdom, and an emergent aerospace ecosystem, Mexico. Results evidence a contrasting categorisation of key enablers among both ecosystems. On the other hand, the automotive ecosystem and geopolitical factors are considered as underpinning enablers for both aerospace ecosystems evolution.

Interpretive structural modeling of supply chain leagility during COVID-19

In the context of COVID-19 crisis, the Moroccan automotive ecosystem is uncertain and volatile. To survive in this changed environment, automotive organizations should manage effectively their leagile supply chains. This management depends mainly on the identification of the leagile enablers influencing the supply chain. To understand the relationship between these enablers, we used interpretive structural modeling (ISM) approach followed by MICMAC analysis. The presentation of a case study from the Moroccan automotive industry illustrates this approach. As a result, we classified enablers according to their driving and dependence power. This classification helps supply chain managers to improve supply chain leagility and performance in order to meet quickly the changed customer needs.

B-FERL: Blockchain based framework for securing smart vehicles

The ubiquity of connecting technologies in smart vehicles and the incremental automation of its functionalities promise significant benefits, including a significant decline in congestion and road fatalities. However, increasing automation and connectedness broadens the attack surface and heightens the likelihood of a malicious entity successfully executing an attack. In this paper, we propose a Blockchain based Framework for sEcuring smaRt vehicLes (B-FERL). B-FERL uses permissioned blockchain technology to tailor information access to restricted entities in the connected vehicle ecosystem. It also uses a challenge–response data exchange between the vehicles and roadside units to monitor the internal state of the vehicle to identify cases of in-vehicle network compromise. In order to enable authentic and valid communication in the vehicular network, only vehicles with a verifiable record in the blockchain can exchange messages. Through qualitative arguments, we show that B-FERL is resilient to identified attacks. Also, quantitative evaluations in an emulated scenario show that B-FERL ensures a suitable response time and required storage size compatible with realistic scenarios. Finally, we demonstrate how B-FERL achieves various important functions relevant to the automotive ecosystem such as trust management, vehicular forensics and secure vehicular networks.

Shareholder Value Effects of the Volkswagen Emissions Scandal on the Automotive Ecosystem

This study provides empirical evidence on the effect of the September 2015 Volkswagen diesel emissions scandal on the stock prices of publicly traded firms in the global automotive ecosystem. We focus on both the supply chain partners of VW—tier‐1 suppliers; tier‐2 suppliers; and business customers—and three groups of firms that are not VW supply chain partners—other motor vehicle manufacturers; parts manufacturers not identified as VW suppliers; and wholesalers, retailers, and rental agencies not identified as VW customers. We find that tier‐1 suppliers of direct material to VW suffered a mean stock price reaction of ‒2.69% in the week following the scandal, but this effect varied by region. European suppliers were the most impacted with a mean stock price reaction of ‒5.52%. Suppliers with larger revenue dependence on VW experienced greater negative stock price reactions, as did suppliers of components for engines and/or emissions systems. Non‐VW parts manufacturers experienced a positive effect. We find a mean stock price reaction of ‒5.28% to VW’s European customers, but no significant effects for non‐VW customers. European motor vehicle manufacturers experienced a mean stock price reaction of ‒7.60%. Our results suggest that firms should not just focus on selecting and monitoring responsible suppliers but also apply some of the same principles to developing responsible customers. Our work also has implications for industry groups, regulators, and legal systems, entities that have the resources and capabilities to effectively monitor large firms to reduce illegal or irresponsible behavior such as the VW scandal.

A Supply Chain in Flux

How will tier-1 suppliers weather a changing automotive ecosystem.

Simulating the battery price and the car-mix in key electro-mobility markets via model coupling

ABSTRACT This paper describes the modelling process of soft-linking two system dynamics models of the automotive ecosystem: the Powertrain Technology Transition Market Agent model and the Transport, Energy, Economics, Environment model. The objective of this work is to explore future battery electric and plug-in hybrid electric powertrain deployment in key electro-mobility markets: China, European Union, India, Japan, Norway and the United States. In the “Alternative” scenario, a combined stock of almost 130 million electric cars in 2030 is simulated. Yet, conventional car technology continues to dominate the car-mix. By soft-linking the two models and adding information from the electric bus market, a more realistic representation of the historical battery price evolution is obtained. Due to the uncertain evolution of the battery price, sensitivity analyses are performed. We conclude that the battery price remains a crucial explanatory variable for annual electric car sales in simulation exercises.

IT Security for the Entire Automotive Ecosystem

IT Security for the Entire Automotive Ecosystem