Browse Topic: Data privacy

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Privacy-Preserving UAV-Based Traffic Data Acquisition Platform2026-99-05957/10/2026
This paper puts forward a Privacy-Preserving UAV-Based Traffic Data Acquisition Platform to address 1) privacy leakage, 2) limited scenario coverage, and 3) low traffic data utilization efficiency in urban traffic monitoring environments. Our system integrates three innovations: 1) Dynamic Privacy Masking (DPM) and Dual-Track acquisition (DTC), which hides sensitive information (e.g., faces, license plates or LPL) in real-time while preserving critical traffic data (e.g., vehicle density, speed), 2) traffic data Localization (DL) and Privacy-Enhanced Federated Learning (FEFL), enabling cross-regional collaboration without raw traffic data sharing by perturbing neural network updates with differential privacy (DP), and 3) Ground-Air Collaboration (GAC) and VPF (VPF), combining UAVs with ground sensors and digital twins (DTs) to cover blind spots (e.g., tunnels, extreme weather). Experimented on UA-DETRAC and CitySim traffic data-sets, the platform achieves 92% privacy compliance (GDPR/PIPL), 87.5% mAP accuracy, and 85% road network coverage, outperforming other methods (e.g., FedUAV, static blurring). It supports applications such as traffic flow optimization, accident prevention, and regulatory alignment.
Zhang, Shilin, Yan, Ming
Privacy-Aware Building AutomationTBMG-547093/1/2026
Researchers at the University of Tokyo developed a framework to enable decentralized artificial intelligence-based building automation with a focus on privacy. The system enables AI-powered devices like cameras and interfaces to cooperate directly, using a new form of device-to-device communication. In doing so, it eliminates the need for central servers and thus the need for centralized data retention, often seen as a potential security weak point and risk to private data.
Decentralized Payment Infrastructure for Electric Vehicle Charging via Blockchain and Smart Contracts2026-28-01262/12/2026
As electric vehicles adoption becomes more common, power grid operators are facing new challenges in managing the unpredictable and varying energy demands in the existing electrical infrastructure. Moreover, the cost of Electric vehicle is high when compared to fuel vehicle it has limited access to charging infrastructure along with the driving range that act as a key barrier preventing the drivers from making shift to EVs. When the EV usage integrates with blockchain, it mitigates the limitation in charging station infrastructure along with the former problem discussed. The lack of trust exists between EV owners and charging station providers can be solved through secure and transparent payment processing possible by blockchain based smart contract. Building charging station on blockchain will ease the automated payment through the use of smart contract and create more efficient EV charging network. Also, the blockchain-based charging system would enable EV owners know if they are being charged in excess and Prosumer know if they are being underpaid. The high initial cost is another prominent issue within the market place. To address this issue the introduction of sharing economy to the EV industry showcases another innovative solution that blockchain offers. The blockchain enabled sharing economy platform allows individuals to access collaboratively with the prosumer and the consumer. This provides alternative to traditional ownership while reduces individual financial barriers and maximizing electric vehicle utilization across the network. The EV users have great opportunity worldwide to take a stake in the future of EV adoption on blockchain. Therefore, this work demonstrates the sharing economy while designing, building, and customizing smart contracts for prosumers and consumers by enabling decentralized payment systems. Our research aims to develop decentralized charging electronic payment systems using blockchain and customized smart contracts to build and design the application. For blockchain Solidity programming language is used. The application displays the charging process, payment system, and charging history information.
Govindasamy, Dhivya, R, Rajarajeswari
Vehicle Usage Digital Twins: Mobility Analysis Test Case2026-26-03941/16/2026
In the evolving landscape of the automotive industry, this study presents an innovative approach to developing digital twins for driver profiles, establishing a standardized and scalable procedure for collecting and analyzing driving data on a global scale. The proposed methodology centers on the development of a robust cloud infrastructure, including Data Lake and associated services, designed for efficient storage and processing of large volumes of data from multiple markets and vehicle types. The research introduces an adaptable procedure for data collection campaigns, applicable to diverse global markets and encompassing a wide range of vehicles, from internal combustion engines to electric and hybrid models. A key feature of this approach is the establishment of advanced data decoding protocols, enabling precise interpretation of CAN network information from vehicles of different manufacturers and models, even when the CAN structure is not previously known. The study defines standardized parameters for data recording, ensuring comparability across different markets and vehicle types, while developing adaptive analysis methodologies to identify specific driving patterns based on vehicle segment, propulsion technology, and demographic characteristics. This comprehensive approach is underpinned by a framework that ensures compliance with data protection regulations globally, facilitating ethical and legal data management across different jurisdictions. The anticipated outcomes include the creation of a highly flexible data-as-a-service platform capable of integrating and analyzing driver data worldwide, and the establishment of a standardized procedure for characterizing driver profiles. The development of advanced vehicular data decoding capabilities allows for the inclusion of a wide variety of brands and models, enabling truly global insights into driver behavior. This study lays the groundwork for a global understanding of driver behavior, providing automotive manufacturers with a powerful tool to adapt their designs to the needs of users worldwide, accelerating innovation in vehicle design and improving the safety of future vehicles.
Arturo, Rubio, Marín Saltó, Anna, Diaz, Francisco, Olivencia, Sergio
A Dynamic Cybersecurity Benchmarking Framework for Automotive Cyber-Physical Systems2026-26-06121/16/2026
The modern vehicle is no longer a mechanical appliance—it has transformed into a software-defined cyber-physical system, integrating OTA updates, cloud-connected diagnostics, V2X services, and telematics-driven personalization. While this evolution promises unprecedented value in consumer experience and fleet operations, it also surfaces a dramatically expanded and evolving attack perimeter, especially across safety-critical ECUs and communication buses. Cyber vulnerabilities have shifted from isolated IT threats to real-time, embedded exploits. Controller area network (CAN), the backbone of vehicle bus systems, remains intrinsically insecure due to its lack of authentication and encryption, making it highly susceptible to message injection and denial-of-service by low-cost tools. Similarly, OEM implementations of BLE-based passive entry systems have proven vulnerable to replay and spoofing attacks with minimal hardware. In the Indian context, the transition to connected mobility is advancing rapidly under national mandates such as FAME II, PM e-DRIVE, and the National Electric Mobility Mission Plan (NEMMP). However, field-level assessments of Indian and international vehicle models—including ICE cars, electric two-wheelers, and fleet EVs—reveal critical gaps in CAN architecture connected to critical ECUs, Cloud API and Endpoints and RF controls. Notably, many of these vulnerabilities materialized after vehicle homologation, propagating through OTA updates or third-party app integrations. This reality underscores the inadequacy of static, pre-market cybersecurity assessments in effectively mitigating operational risk. This paper introduces a novel, scalable methodology that addresses this critical gap by enabling empirical, attack-informed validation, aligned with both Indian priorities and international best practices
Shah, Ravindra, Awasthi, Vibhu Vaibhav, Karle, Ujjwala
Cybersecurity in Electric Vehicle Charging Infrastructure: A Review of Threats, Vulnerabilities, and Mitigation Strategies2026-26-06141/16/2026
The rapid expansion of electric vehicle (EV) charging infrastructure introduces complex cybersecurity challenges across hardware, software, network, and cloud layers. This review paper synthesizes existing research, standards, and documented incidents to identify critical vulnerabilities and propose layered mitigation strategies. We present a structured threat taxonomy based on the STRIDE model, enriched with real-world attack vectors and mapped to mitigation controls. Our analysis spans physical tampering, insecure firmware updates, protocol-level flaws in OCPP and ISO 15118, and cloud misconfigurations. While prior studies often focus on isolated domains, this work unifies fragmented insights into a cohesive framework. We highlight gaps in current literature, such as inconsistent adoption of secure protocols and limited validation of EVSE identity formats. By aligning threats with industry standards (SAE J3061, NIST CSF, IEC 62443) and scoring risks using CVSS v3.1, we offer a practical roadmap for manufacturers, operators, and policymakers. The paper concludes with recommendations for future research, including experimental validation, blockchain-based audit trails, and AI-driven anomaly detection.
Aggarwal, Akshit, Gupta, Saurabh, Sirohi, Kapil, Arisetty, Venkatesh, Chatterjee, Avik
Artificial Intelligence in Passenger Cars: Unlocking Intelligent Mobility and Human-Centric Vehicle Innovation2026-26-06721/16/2026
Artificial Intelligence (AI) is radically transforming the automotive industry, particularly in the domain of passenger vehicles where personalization, safety, diagnostics, and efficiency. This paper presents an exploration of AI/ML applications through quadrant of the key pillars: Customer Experience (CX), Vehicle Diagnostics, Lifecycle Management, and Connected Technologies. Through detailed use cases, including AI-powered active suspension systems, intelligent fault code prioritization, and eco-routing strategies, we demonstrate how AI models such as machine learning, deep learning, and computer vision are reshaping both the user experience and engineering workflow of modern electric vehicles (EVs). This paper combines simulations, pseudo-algorithms and data-centric examples of the combined depth of functionality and deployment readiness of these technologies. In addition to technical effectiveness, the paper also discusses the challenges at field level in adopting AI at scale i.e., data scarcity, regulatory, sensory fusion reliability, and user trust. The set of recommendations on safe, modular, and scalable integration roadmap, including the importance of continual learning, hybrid digital twins, and legacy-system interoperability, is provided. By offering a comprehensive yet application-driven perspective, this work serves as both a technical reference and strategic blueprint for stakeholders aiming to embed intelligent systems across the vehicle lifecycle, from predictive diagnostics to real-time adaptive user interfaces.
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Next-Gen Automotive: Enablers and Monetization for Automotive SaaS and Software Defined Vehicles2026-26-06871/16/2026
Commercial vehicles form the backbone of global supply chains. In India, the commercial vehicle (CV) industry is at a transformative crossroads, evolving from traditional hardware-centric models to advanced, software-defined architectures. Central to this shift are Software-Defined Vehicles (SDVs) and Automotive Software-as-a-Service (SaaS), catalysing a move toward intelligent, connected, and highly productive mobility solutions. With the Indian CV market surpassing $50 billion in 2024 and witnessing robust growth due to expanding e-commerce, infrastructure projects and regulatory evolution. Indian original equipment manufacturers (OEMs) are spearheading this revolution. This paper presents a comprehensive analysis of the technological enablers, monetization strategies, distinct challenges and opportunities encountered by Indian OEMs during their shift toward SDVs and automotive SaaS based business models. This research also examines the most important technical pillars underpinning next-generation automotive ecosystem creation and these pillars are centralized computing infrastructures, embedded cloud integration, efficient over-the-air (OTA) update engines and enhanced cybersecurity models designed to protect larger numbers of connected vehicles are observed. This work explains, from a financial standpoint, the new and innovative methods in which OEMs and technology providers are leveraging SDVs and SaaS to generate new revenue streams. The prominent strategies being debated are Feature-on-Demand (FoD) services, subscription-based services based on different functionalities and features, the creation of dynamic in-vehicle app ecosystems, data monetization opportunities based on privacy regulations and flexible pay-per-use models. Additionally, the changing paradigm of Mobility-as-a-Service (MaaS) model is comprehensively analysed in terms of its impact on the industry of the future. Yet, this revolutionary process is plagued by a number of challenges. The paper offers a critical analysis of concerns like the necessity of achieving widespread customer acceptance of new service models, the complexities of complying with diverse data privacy regulations.
Saini, Gourav, Jahagirdar, Shweta, Khandekar, Dhiraj Baburao
Fed-BNGC: A Federated Clustering Algorithm to Solve the Problem of User Heterogeneity2025-99-011011/11/2025
Federated learning is an emerging distributed machine learning framework that allows edge devices to co-train global models without uploading their own data to a central server, which protects users’ data privacy. However, the problem of federated learning is that there is too much heterogeneity between users, including the size of the data, the different model structure, and the quality of the device. This problem leads to the need for more communication to train a better model, which also increases the cost of communication. To solve this problem, we developed the Fed-BNGC algorithm. First of all, the algorithm can perform the first screening according to the difference between the user’s system and the amount of data, and select the users with poor system evaluation value, and these users will not participate in this training. Secondly, the second screening is carried out according to the difference of the user’s model parameters, and the user with the greatest difference from other users is selected and eliminated. Finally, the K-means++ clustering algorithm is used to cluster users with similar model parameters, and a model that is more suitable for this clustering user is jointly trained. Compared with the Fedavg and Fedprox algorithms, the accuracy of our method is improved to varying degrees, and the number of communication is greatly reduced.
Yang, Shuangyu, Kan, Zhongliang
Influence of MPC Based Driver Types on the Energy Consumption of Battery Electric Vehicles2025-24-01139/7/2025
Electric vehicles are increasingly important for emission reduction and the promotion of sustainable mobility. Despite their advantages over conventional vehicles, the energy consumption of electric vehicles is heavily influenced by various factors such as driving behavior, elevation profile, and environmental conditions. In particular, the driving style plays a crucial role in determining range and energy consumption. This influence is also observed in the context of the Interreg project FreeE-Bus. This project focuses on the development of optimized charging management for electric buses in the public transport system of the Lake Constance region. Due to strict data protection regulations that prevent a detailed analysis of driver data, assessing the impact of driving styles is difficult. This paper addresses this issue by developing an innovative driver model that simulates different driver types and analyzes their effects on energy consumption. The driver model employs a Model Predictive Control approach, and two driver types are implemented and tested on a real vehicle. The results of this study demonstrate that a simulation of different driving styles is possible and highlight significant differences in energy consumption, providing valuable insights into improving driving strategies. Therefore, this approach represents a valuable addition to the existing range of driver control modeling methods.
Konzept, Anja, Reick, Benedikt, Miller, Marius, Rautenberg, Philip, Störzer, Martin
Use of Model-Based Datasets and Neural-Networks to Support Real-Time Driving Suggestions System for Components Preservation in Battery Electric Vehicles2025-24-01159/7/2025
Nowadays, Battery Electric Vehicles (BEVs) are considered an attractive solution to support the transition towards more sustainable transportation systems. Although their well-known advantages in terms of overall propulsion efficiency and exhaust emissions, the diffusion of BEVs on the market is still reduced by some technical bottlenecks. Among those, the uncertainty about the expected durability of the vehicle's onboard battery packs plays a key role in affecting customer choice. In this context, this paper proposes the use of model-based datasets for training a driving support system based on machine learning techniques to be installed on board. The objective of this system is to acquire vehicle, environmental, and traffic information from sensor’ networks and provide real-time smart suggestions to the driver to preserve the remaining useful life of vehicle components, with particular reference to the battery pack and brakes. For the generation of the training dataset, first, a set of onboard measurements is performed with the vehicle running in different operational conditions in terms of driving style, environmental temperature, road surface, and traffic intensity. Then, experimental tests are carried out to parametrise and validate battery electro-thermal simulation models, which are used, in combination with an electric vehicle model and the related brake-wearing sub-model, to perform long-term analysis through multiple runs of the acquired driving cycles. The proposed system employs federated learning to enhance prediction models while preserving data privacy. Vehicles contribute locally trained parameters to a global model, reducing data transfer overhead and adapting to evolving driving conditions. Federated averaging minimises model drift across clusters, ensuring consistency. Edge computing processes data locally, enabling low-latency decision-making. Optimised neural networks ensure efficient execution on embedded systems, enhancing real-time driver support. By integrating federated learning and edge AI, the system achieves robust, scalable, and privacy-preserving optimisation for next-generation electric mobility.
Bernardi, Mario Luca, Capasso, Clemente, Iannucci, Luigi, Sequino, Luigi
Legal Issues and Policy: Vol. 3EPRCOMPV0520244/30/2025
Abdul Hamid, Umar Zakir, Eastman, Brittany
Uncovering Security Flaws in DC Chargers for Electric Vehicles2025-01-81184/1/2025
The added connectivity and transmission of personal and payment information in electric vehicle (EV) charging technology creates larger attack surfaces and incentives for malicious hackers to act. As EV charging stations are a major and direct user interface in the charging infrastructure, ensuring cybersecurity of the personal and private data transmitted to and from chargers is a key component to the overall security. Researchers at Southwest Research Institute® (SwRI®) evaluated the security of direct current fast charging (DCFC) EV supply equipment (EVSE). Identified vulnerabilities included values such as the MAC addresses of both the EV and EVSE, either sent in plaintext or encrypted with a known algorithm. These values allowed for reprogramming of non-volatile memory of power-line communication (PLC) devices as well as the EV’s parameter information block (PIB). Discovering these values allowed the researchers to access the IPv6 layer on the connection between the EV and EVSE and use traditional ethernet penetration testing methods, including port and vulnerability scanning. Port scanning exposed open SSH and HTTP services, the latter of which was vulnerable and allowed unauthenticated retrieval of proprietary information. The ports should be secured, or closed if unneeded, to prevent this type of vulnerability.
Kozan, Katherine
Privacy Risk Assessment of Travel Trajectories in Metro AFC Data2025-01-72142/21/2025
The rapid expansion of metro systems in major cities worldwide has resulted in the accumulation of vast amounts of travel data through Automatic Fare Collection (AFC) systems. While this data is crucial for enhancing and optimizing transportation networks, it also raises significant concerns regarding passenger privacy due to the potential exposure of individual travel patterns. In this paper, we propose a novel privacy risk assessment model aimed at quantifying the uniqueness of travel trajectories and evaluating the associated privacy threats. Utilizing AFC data from Chengdu collected in March 2021, we first employ an information entropy approach to assess the uniqueness of travel trajectories across different time granularities. We then apply the K-Means clustering algorithm to classify these trajectories into categories based on their uniqueness levels, enabling us to investigate how factors like travel time and routes influence trajectory uniqueness. To further understand the privacy implications, we simulate attacker scenarios by replicating the process of identifying users based on known travel trajectories, thereby assessing the risk of privacy exposure under various time scales. Our experimental results reveal that metro travel trajectories exhibit high uniqueness at finer time resolutions, and that travel routes significantly affect this uniqueness. Notably, even at coarser time granularities, nearly half of the users remain susceptible to identification risks. These findings highlight the critical need for effective privacy protection strategies in the management of AFC data. The insights provided by this study are essential for policymakers and transit authorities seeking to safeguard passenger privacy while leveraging AFC data for transportation improvements.
Fan, Xiaoting, Qu, Xu, Yang, Hongtai
2025 Trends in Healthcare: Bridging Technology, Personalization, and AccessibilityTBMG-524602/1/2025
The emergence of data-driven healthcare promises predictive and preventive care through enhanced data integration and analytics. This trend means that medical device companies must navigate challenges related to data privacy and operational efficiency while transitioning to a data-centric approach. Artificial intelligence (AI) is spearheading this shift toward hyper-personalized medicine, enabling precision treatments based on genetic profiles and predictive analytics for early disease detection. Advancements in telemedicine, AI, wearable technology, and data analytics, are reshaping how care is delivered, making it more accessible, personalized, and efficient in 2025.
653P2-5 Avionics Application Software Standard Interface, Part 2, Extended ServicesARINC653P2-5 (Current)12/16/2024
As avionics software continues to evolve, so does ARINC Specification 653. ARINC 653 Part 2 specifies extensions (i.e., optional services) to the required Application Program Interfaces (APIs) described in ARINC 653 Part 1.
Airlines Electronic Engineering Committee
Automotive Cybersecurity: Defend the Future of Connected Vehicles2024-28-012312/5/2024
Cybersecurity, particularly in the automotive sector, is of paramount importance in today’s digital age. With the advent of connected commercial vehicles, which leverage telematics for efficient fleet management, the landscape of automotive cybersecurity is rapidly evolving. These vehicles, integral to logistics and transportation businesses, are becoming increasingly connected, thereby escalating the risks associated with cybersecurity threats. These commercial vehicles are becoming prime targets for cyber-attacks due to their connectivity and the valuable data they hold. The potential consequences of these cyber-attacks can range from data breaches to disruptions in fleet operations, and even safety risks. This paper analyses the unique challenges faced by the commercial vehicle sector, such as the need for robust telematics systems, secure communication channels, and stringent data protection measures. Case studies of notable cybersecurity incidents involving commercial vehicles are presented, providing valuable insights into the modus operandi of cybercriminals. Strategies and best practices to mitigate these risks are proposed, emphasizing the need for secure vehicle architecture and design, intrusion detection systems, and regular OTA updates. The role of employee training programs in enhancing cybersecurity awareness is also highlighted. Emerging trends like AI and machine learning in threat detection, blockchain technology for secure data transmission, and collaborations with ethical hackers for vulnerability assessment are discussed. The paper reviews the current regulatory landscape, stressing the need for international standards specifically for connected commercial vehicles. It concludes with an outlook on anticipated developments in automotive cybersecurity, recommendations for industry stakeholders, and the assertion that prioritizing cybersecurity is crucial for the future of the commercial vehicle industry.
Mahendrakar, Shrinidhi, Madarla, Manoj, Gangapuram, Siva, Dadoo, Vishal
Understanding the Vulnerability in Wireless Backhaul Networks for 5GTBMG-5219912/1/2024
A research team led by Rice University’s Edward Knightly has uncovered an eavesdropping security vulnerability in high-frequency and high-speed wireless backhaul links, widely employed in critical applications such as 5G wireless cell phone signals and low-latency financial trading on Wall Street.
Cyber Security Challenges in V2C and in Vehicle Network2024-28-003510/17/2024
Based on advanced Automotive functionality, Vehicle networks has enabled the exchange of data to multiple domains and to meet these demands, more complex software applications, some of which require service-based cloud are developed. Exposure of data creates multiple threats for attacker to tamper security and privacy. Automotive cybersecurity topic has gained momentum based on multiple gaps identified in Automotive In vehicle and around the vehicle networks. In this paper, we provide an extensive overview on V2C (Vehicle to Cloud) and In-vehicle data protection, we also highlight methods to identify threats on any vehicle network connected to V2C and identify methods to verify security functionality using Fuzz or Penetration test protocol, we have identified gaps in existing security solutions and outline possible open issues and probable solution.
Panda, Jyotiprakash, Jain, Rushabh Deepakchand
A Methodology to Enhance Transparency for Trustworthy Artificial Intelligence for Cooperative, Connected, and Automated Mobility12-08-01-00109/2/2024
In this research, we propose a set of reporting documents to enhance transparency and trust in artificial intelligence (AI) systems for cooperative, connected, and automated mobility (CCAM) applications. By analyzing key documents on ethical guidelines and regulations in AI, such as the Assessment List for Trustworthy AI and the EU AI Act, we extracted considerations regarding transparency requirements. Recognizing the unique characteristics of each AI system and its application sector, we designed a model card tailored for CCAM applications. This was made considering the criteria for achieving trustworthy autonomous vehicles, exposed by the Joint Research Centre (JRC), and including information items that evidence the compliance of the AI system with these ethical aspects and that are also of interest to the different stakeholders. Additionally, we propose an MLOps Card to share information about the infrastructure and tools involved in creating and implementing the AI system.
Cañas, Paola Natalia, Nieto, Marcos, Otaegui, Oihana, Rodriguez, Igor
Synthetic On Board Diagnostics Data Generation and Evaluation for Vehicle Diagnostic TestingSAE-PP-004058/21/2024
The generation of data plays a vital role in Machine Learning (ML) techniques by providing the foundation for training and improvement of forecast models. As one application area for these models, in-vehicle systems, like vehicle diagnostics, have the potential to enhance the reliability and durability of vehicles by utilizing ML models in the testing phases. However, acquiring a high volume of quality On-Board Diagnostics (OBD) data is time-consuming and poses challenges like the risk of exposing sensitive information. To address this issue, synthetic data generation offers a promising alternative that is already in use in other domains. Thereby, synthetic data allows the exploitation of knowledge found in original data, ensuring the privacy of sensitive data, with less time costs of data acquisition. For this purpose, the research presented in this contribution investigates the use of statistical and ML-based synthetic OBD data generation methods. The models are evaluated with the custom-developed evaluation method that fits the attributes of the OBD data used. Finally, an important result is the successful generation of synthetic OBD data that can be used to enhance the SAE J1699 OBD compliance test, together with tools and insights for models and evaluation.
Vucinic, Veljko, Hantschel, Frank, Kotschenreuther, Thomas
Privacy for Software-defined Battery Electric VehiclesEPR20240126/17/2024
The integration of software-defined approaches with software-defined battery electric vehicles brings forth challenges related to privacy regulations, such as European Union’s General Data Protection Regulation and Data Act, as well as the California Consumer Privacy Act. Compliance with these regulations poses barriers for foreign brands and startups seeking entry into these markets. Car manufacturers and suppliers, particularly software suppliers, must navigate complex privacy requirements when introducing vehicles to these regions. Privacy for Software-defined Battery Electric Vehicles aims to educate practitioners across different market regions and fields. It seeks to stimulate discussions for improvements in processes and requirements related to privacy aspects regarding these vehicles. The report covers the significance of privacy, potential vulnerabilities and risks, technical challenges, safety risks, management and operational challenges, and the benefits of compliance with privacy standards within the software-defined battery electric vehicle realm. Click here to access the full SAE EDGETM Research Report portfolio.
Abdul Hamid, Umar Zakir
Facial Recognition Software and Privacy Law in Transportation TechnologyEPR20240115/7/2024
Data privacy questions are particularly timely in the automotive industry as—now more than ever before—vehicles are collecting and sharing data at great speeds and quantities. Though connectivity and vehicle-to-vehicle technologies are perhaps the most obvious, smart city infrastructure, maintenance, and infotainment systems are also relevant in the data privacy law discourse. Facial Recognition Software and Privacy Law in Transportation Technology considers the current legal landscape of privacy law and the unanswered questions that have surfaced in recent years. A survey of the limited recent federal case law and statutory law, as well as examples of comprehensive state data privacy laws, is included. Perhaps most importantly, this report simplifies the balancing act that manufacturers and consumers are performing by complying with data privacy laws, sharing enough data to maximize safety and convenience, and protecting personal information. Click here to access the full SAE EDGETM Research Report portfolio.
Eastman, Brittany
Evaluating Network Security Configuration (NSC) Practices in Vehicle-Related Android Applications2024-01-28814/9/2024
Android applications have historically faced vulnerabilities to man-in-the-middle attacks due to insecure custom SSL/TLS certificate validation implementations. In response, Google introduced the Network Security Configuration (NSC) as a configuration-based solution to improve the security of certificate validation practices. NSC was initially developed to enhance the security of Android applications by providing developers with a framework to customize network security settings. However, recent studies have shown that it is often not being leveraged appropriately to enhance security. Motivated by the surge in vehicular connectivity and the corresponding impact on user security and data privacy, our research pivots to the domain of mobile applications for vehicles. As vehicles increasingly become repositories of personal data and integral nodes in the Internet of Things (IoT) ecosystem, ensuring their security moves beyond traditional issues to one of public safety and trust. To provide a view of the current vehicle apps security landscape, we delve into 122 vehicle-related apps, grouping them into three distinct categories: official car apps developed by manufacturers, general car-related apps, and OBD-II diagnostic tool apps. Our findings show that 68.85% of apps utilize NSC with varying degrees of NSC customization and security practices across these categories. Additionally, understanding that frequent updates often correlate with active maintenance and potential security patching, we analyze the update frequencies of the top ten downloaded apps in each category. The results provide valuable insight into app developers’ level of commitment to safety in the evolving automotive ecosystem. This research aims to drive awareness, underline existing security NSC practices, and pave the way for a more secure vehicular app environment.
Zhang, Linxi, Ma, Di
Opportunities, Challenges and Requirements for Use of Blockchain in Unmanned Aircraft Systems Operating Below 400ft Above Ground Level for Commercial Use2024-01-19283/5/2024
The number of Unmanned Aircraft Systems (UAS) has been growing over the past few years and will continue to grow at a faster pace in the near future. UAS faces many challenges in certification, airspace management, operations, supply chain, and maintenance. Blockchain, defined as a distributed ledger technology for the enterprise that features immutability, traceability, automation, data privacy, and security, can help address some of these challenges. However, blockchain also has certain drawbacks and, additionally, it is still not fully mature. Hence it is essential to study how blockchain can help UAS. This Aerospace Information Report (AIR) presents the current opportunities, challenges of UAS operating at or below 400 ft Above Ground Level (AGL) altitude for commercial use and how blockchain can help meet these challenges. It also provides requirements for developing a blockchain solution for UAS along with the need for the standardization of blockchain enabled processes.
Rencher, Robert, Manoharan, Dinesh, R, Prithiviraj, Ghimire, Ritesh, Markou, Chris, Fabre, Chris, Roboff, Mark, Budeanu, Dragos, Walthall, Rhonda, Veluri, Sastry
Integrating Ergonomic and Artificial Intelligence in the Automotive2023-36-00421/8/2024
The integration of ergonomics and artificial intelligence (AI) in the automotive industry has the potential to revolutionize the way how vehicles are designed, manufactured and used. The aim of this article is to review the recent literature on the subject and discuss the opportunities and challenges presented by the integration of these two fields. The paper begins defining the ergonomics and the AI and providing an overview of their respective roles in the automotive industry. It then examines the benefits of the integration of ergonomics and AI in the automotive industry, including the optimization of vehicle design and manufacturing process. The enhancement of the driver experience, and improvement of safety accessibility, and customization, however, the integration of ergonomics and AI in the automotive industry also presents challenges, including ethical and legal considerations, data privacy, liability, and the impact on the employment in the automotive industry. The paper reviews research on these challenges and suggests that the development of international standards for the integration of AI in the vehicles may be necessary to ensure that AI systems in vehicle are secure, highlighting the need for future research to explore the integration of ergonomic and AI in the automotive industry. Future research should focus and addressing the ethical, legal, and societal implications of the AI in vehicles, as well as exploring new opportunities for the use of AI in design, manufacturing, and use of vehicles in overall, the integration of ergonomics and AI in the automotive industry has the potential to significantly improve the design and manufacturing of vehicles, as well as enhance the driving experience for users. However, the integration of these two fields also poses challenges that must be addressed, including ethical concerns, legal considerations, and the employment in the automotive industry. By working to overcome these challenges, we ensure that benefits of ergonomics and AI in the automotive industry are fully realized while minimizing their potential negative impacts.
Puertas, Carlos Augusto Palermo, Galhardi, Antonio Cesar
Users’ Perception of Data Privacy in Self-Driving Vehicles in Dubai2023-01-190812/29/2023
The concerns surrounding AV adoption encompass the data protection factor. An online survey was conducted to gain insights into this concern, targeting UAE residents with knowledge about Autonomous Vehicle (AV) technology. The collected data were subjected to statistical analysis to provide valuable information for the UAE government and private sectors. To achieve this goal, we conducted a statistical analysis of the collected data, which resulted in further insights regarding the obstacles impeding the adoption of AV technologies in the United Arab Emirates. This analysis further quantifies the factors that contributed to UAE public concerns. We also examined user group evaluations in terms of their propensity to employ the technology in the future.
Hussein, Shuq, Nasiruddeen Muhammad, Nasiruddeen Muhammad, Eman Abu Shabab, Eman, Saad Amin, Saad, Hussain Al-Ahmad, Hussain, Mukhtar, Husameldin, Mohammad Rababa, Mohammad, Burkhard Schafer, Burkhard
Autonomous Vehicle Engineering: November 202323AVEP1111/9/2023
Editorial Automated driving's continuing mission: One step forward… The Navigator Are robotaxis ready for prime time? AV Simulation Faces a Long and Winding Road There are many divergent-but-related ideas about how best to leverage simulation to 'teach' and test automated vehicles. Global AV simulation experts continue to attack the technology's persistent obstacles. DOT's 'Framework' for AV Development The U.S. Department of Transportation establishes structure to advance collaborative AV testing. The Chaos of Automotive Data Privacy Regulators and other privacy advocates believe vehicle-related data collection and brokering is in overdrive. One expert believes a day of reckoning is coming. Simulation Developer rFpro Mimics Vehicle Sensors 'Ray-tracing' software enables the training of ADAS systems entirely by simulation. Eight of the top 10 OEMs are using it. New Tools & Technologies
Opportunities, Challenges and Requirements for Use of Blockchain in Unmanned Aircraft Systems2023-01-15049/5/2023
Unmanned Aircraft Systems (UAS) have been growing over the past few years and will continue to grow at a faster pace in future. UAS faces many challenges in certification, airspace management, operations, supply chain, and maintenance. Blockchain, defined as a distributed ledger technology for the enterprise that features immutability, traceability, automation, data privacy, and security, can help address some of these challenges. However, blockchain also has certain challenges and is still evolving. Hence it is essential to study on how blockchain can help UAS. G-31 technical committee of SAE International responsible for electronic transactions for aerospace has published AIR 7356 [1] entitled Opportunities, Challenges and Requirements for use of Blockchain in Unmanned Aircraft Systems Operating below 400ft above ground level for Commercial Use. This paper is a teaser for AIR 7356 [1] document. It presents the current opportunities, challenges of UAS operating at or below 400 ft Above Ground Level (AGL) altitude for commercial use and how blockchain can help meet these challenges. It also provides requirements for developing a blockchain solution for UAS along with the need for the standardization of blockchain enabled processes.
Manoharan, Dinesh, G.V.V., Ravi Kumar, R, Prithiviraj, Ghimire, Ritesh, Rencher, Robert, Markou, Chris, Fabre, Chris, Roboff, Mark, Budeanu, Dragos, Rajamani, Ravi, Walthall, Rhonda, Veluri, Sastry
ADAS Sensor Data Handling in the World of Autonomous Mobility2023-01-09933/7/2023
By 2030, about 95% of new vehicles sold globally will be connected, up from around 50% today. Around 45% of these vehicles will have intermediate and advanced connectivity features (source: McKinsey, 2021). Modernization, standardization, and automation are the key steps in the roadmap of data handling for connected vehicles. Vehicle software increasingly sits within a connected ecosystem of devices. Consumer expectations are shifting more towards digital compatibility, connectivity, and new functionalities offered in autonomous vehicles. Digitalization is turning the vehicles of the future into commodities that are as experimental as they are useful. Many OEMs are at the beginning of this transformation journey and have struggled on the software side of things. The entire automotive industry is putting its efforts into effectively monetizing the data captured during the development and management of autonomous vehicles. It is not easy to handle the complexity, elasticity, and volume of data involved. We are now realizing the possibilities of connected vehicles. Soon, the day will come when data no longer has to be stored directly in the vehicles, and this will naturally result in an enormous advantage in terms of performance and cost improvements for OEMs. However, the challenge is how the automotive industry will manage this transformation and maximize the inherent value of this huge amount of data. E.g., one single car can generate up to 1 TB of data in an hour. Traditional data storage methods simply cannot effectively manage costs to meet all the needs of customer experience and expectations. Leveraging the ADAS sensor data to speed up innovation and improve the customer experience will call for totally new capabilities and infrastructure. Load balancing and failover management are used to achieve data protection, integrity, and availability. Data is one of the assets of an organization, but without robust data handling, the right strategy can detect problems and automatically provide insights into your data center. This paper will give an excellent overview of how to handle petabytes of data on a daily basis in an automatic way with proper utilization of infrastructure and HPC resources and minimum manual tasks. I will describe how to handle data in a hybrid work model. hybrid if you have a data center on premises and, in the next stage, you want to go to any of the cloud data storage options because of time constraints, customer demand, or a third-party company involved in the data sharing concept. how to handle the number of HDDs in both data centers in an effective way without any impact on legacy systems and with complete data integrity.
Samantaray, Rojalin
Opportunities, Challenges, and Requirements for Use of Blockchain in Unmanned Aircraft Systems Operating Below 400 Feet Above Ground Level for Commercial UseAIR7356 (Current)1/25/2023
This SAE Aerospace Information Report (AIR) focuses on opportunities, challenges, and requirements in use of blockchain for Unmanned Aircraft Systems (UAS) operating at and below 400 feet above ground level (AGL) for commercial use. UAS stakeholders like original equipment manufacturers (OEMs), suppliers, operators, owners, regulators, and maintenance repair and overhaul (MRO) providers face many challenges in certification, airspace management, operations, supply chain, and maintenance. Blockchain—defined as a distributed ledger technology that includes enterprise blockchain—can help address some of these challenges. Blockchain technology is evolving and also poses certain concerns in adoption. This AIR provides information on the current UAS challenges and how these challenges can be addressed by deploying blockchain technology along with identified areas of concern when using this technology. The scope of this AIR includes elicitation of key requirements for blockchain in UAS across its life cycle and the need for the standardization of blockchain-enabled processes.
G-31 Digital Transactions for Aerospace
Data OwnershipHRCS-N0000018/9/2022
The Health-Ready Components and Systems (HRCS) Consortium developed this policy statement to provide guidance regarding the ownership and use of operating data. These principles apply across all mobility sectors, irrespective of the underlying asset type. Sophisticated components installed in modern vehicles can store and transmit large volumes of data regarding the operating condition of individual components, systems, or the entire vehicle. This data is valuable for predictive and comparative purposes in a variety of contexts. This statement is intended to clarify the rightful owner of that operating data, as well as elucidate some of the key issues relevant to this question.
Health Ready Components and Systems
Legal Issues Facing Automated Vehicles, Facial Recognition, and Privacy RightsEPR20220167/28/2022
Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality. Legal Issues Facing Automated Vehicles, Facial Recognition, and Individual Rights seeks to highlight the benefits of using FRS in public and private transportation technology and addresses some of the legitimate concerns regarding its use by private corporations and government entities, including law enforcement, in public transportation hubs and traffic stops. Constitutional questions, including First, Forth, and Ninth Amendment issues, also remain unanswered. FRS is now a permanent part of transportation technology and society; with meaningful legislation and conscious engineering, it can make future transportation safer and more convenient. Click here to access the full SAE EDGETM Research Report portfolio.
Eastman, Brittany
SAE TOMORROW TODAY: Innovating in Real-Time with Software-Defined Vehicles132367/25/2022
The future of mobility is software-defined. For automakers, this means that vehicles will transition to a combination of software-centric computing systems on wheels. The key to unlocking this incredible opportunity is the capability to update vehicles instantly. Enabling this transformation is Jeffrey Chou, Co-Founder and CEO of Sonatus. With a proven track record of revolutionizing IT data centers, Jeff and his team are bringing to life an extensive array of advanced technologies and a cloud-connected in-vehicle infrastructure that is accelerating the OEM journey toward fully Software-Defined Vehicles (SDVs). The Sonatus Digital Dynamics platform - which was recently integrated into the Hyundai Genesis GV60 - helps automakers create a foundation for dynamic SDVs that can evolve and adapt in real-time, empowering them to innovate faster, reduce complexity and costs, and become more agile. Along with this opportunity comes an increased focus on cybersecurity and data protection. Hear from Jeff on how SDVs will change the future of mobility, the importance of security and data protection amidst this shift, and how OEMs can differentiate themselves to increase competitiveness.
Hineman, Marcie
Security IP ModuleTBMG-458065/1/2022
Curtiss-Wright Defense Solutions Ashburn, VA 703-779-7800
Vulnerability Management - Practical Challenges and Solutions for Cybersecurity EngineeringSAE-PP-001869/29/2021
Integrating cybersecurity development, testing and operations activities around a common risk and vulnerability management framework is key to implementing an effective and efficient cybersecurity engineering process. We describe a case study to investigate the practical implementation of vulnerability management within an automotive cybersecurity engineering process. The case study is based on applying a combination of Cybellum's Product Security Assessment tool for identifying software vulnerabilities with HORIBA MIRA's vulnerability management method. We illustrate how effective vulnerability management requires a combination of people, processes and tools, explore the challenges of scaling this activity and identify opportunities for further development of a scalable solution.
Lazebnik, Eddie, Traitel, Eyal, Wooderson, Paul, Ruddle, Douglas
Efficient On-Premises Data Storage Solution for Autonomous Driving2021-26-00429/22/2021
Total autonomy will be 100% accident free by testing more than 8 Billion miles of sensor data*. However, testing the software in real driving situations on the road would be extremely time consuming, expensive, cumbersome. Numerous improvements of the algorithms are necessary to perfect the control software for autonomous driving. To train and test the algorithms we need sensor data. Here the challenge is to store and manage petabytes of sensor data across the global locations with data integrity. In autonomous vehicle to maintain the road safety, multiple sensors are mounted with varying levels of maturity. Multisensory data is the process of combining observations from different sources to provide a robust and complete description of an environment and to overcome the limitation in term of availability. This paper describes an end to end architecture and design of global data ingest, data processing accessing sensor data effectively, data service and data protection method. It gives a scalable and reliable solution that is capable to handle hundreds of petabytes using hundreds of computer cores. Proposed design helps location-independent data Ingest, faster data availability, On-Demand usage of data effectively and recovery from accidental deletion of data. It helps to store exabytes of data in a tiered storage model in different categorizations. This architecture will reduce it to within 3 to 5 days for further processing of data with data integrity & failover management, Having right strategy will be beneficial in mitigating the risks in sensor data management.
samantaray, Rojalin
Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared VehiclesEPR20210199/13/2021
Modern automobiles collect around 25 gigabytes of data per hour and autonomous vehicles are expected to generate more than 100 times that number. In comparison, the Apollo Guidance Computer assisting in the moon launches had only a 32-kilobtye hard disk. Without question, the breadth of in-vehicle data has opened new possibilities and challenges. The potential for accessing this data has led many entrepreneurs to claim that data is more valuable than even the vehicle itself. These intrepid data-miners seek to explore business opportunities in predictive maintenance, pay-as-you-drive features, and infrastructure services. Yet, the use of data comes with inherent challenges: accessibility, ownership, security, and privacy. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles examines some of the pressing questions on the minds of both industry and consumers. Who owns the data and how can it be used? What are the regulatory regimes that impact vehicular data use? Is the US close to harmonizing with other nations in the automotive data privacy? And will the risks of hackers lead to the “zombie car apocalypse” or to another avenue for ransomware? This report explores a number of these legal challenges and the unsettled aspects that arise in the world of automotive data. Click here to access the full SAE EDGETM Research Report portfolio.
Dukarski, Jennifer
SAE TOMORROW TODAY: Protection from Global Threats: Cyber Terrorism & Personal Security with Bryan Hurd131608/27/2021
Imagine living in a world where everyone knows your name. Our world is becoming more and more connected every day. Ask yourself: if you were a high-profile individual, how would you keep your data secure? What if your day-to-day safety was at risk? Are there bad actors that would be able to take control of your finances or your business? These are the questions that keep Bryan Hurd up at night. As Chief of Office in Seattle for Aon's Cyber Solutions, Hurd and the team help corporations, individuals, and their families stay safe from cyber terrorism and threats using special security details, organizational education, and more. To learn more about the work from the team at Aon, head to https://www.aon.com/cyber-solutions and follow Bryan Hurd on LinkedIn (https://www.linkedin.com/in/bryanhurd/).
Hineman, Marcie
Determination of Cost Benefits from Implementing a Blockchain SolutionARP6984 (Current)8/19/2021
This SAE Aerospace Recommended Practice (ARP) provides insights on how to perform a Cost Benefit Analysis (CBA) to determine the Return on Investment (ROI) that would result from implementing a blockchain solution to a new or an existing business process. The word “blockchain” refers to a method of documenting when data transactions occur using a distributed ledger with desired immutable qualities. The scope of the current document is on enterprise blockchain which gives the benefit of standardized cryptography, legal enforceability and regulatory compliance. The document analyzes the complexity involved with this technology, lists some of the different approaches that can be used for conducting a CBA, and differentiates its analysis depending on whether the application uses a public or a private distributed network. This document is intended for people who do not have a deep technical understanding or familiarity with blockchain solutions to qualify and quantify its economic benefits (i.e., the value proposition).
G-31 Digital Transactions for Aerospace
Requirements for Aviation, Space, and Defense Auditor Training, Development, Competence, and AuthenticationAS9104/3A (Current)11/11/2020
This document defines the minimum requirements for auditors, CBs, Auditor Authentication Bodies (AABs), Training Provider Approval Bodies (TPABs), and Training Providers (TPs) who participate in the IAQG Industry Controlled Other Party (ICOP) scheme. The requirements in this standard supplement those defined within the 9104/1, 9104/2, ISO/IEC 17021-1, and ISO/IEC 17021-3 standards. Data protection for the parties subject to this document and other relevant requirements of the ICOP scheme are managed via bi-lateral contracts between the joint controllers of the data.
G-14 Americas Aerospace Quality Standards Committee (AAQSC)
Challenges in Integrating Cybersecurity into Existing Development Processes2020-01-01444/14/2020
For an established development process and a team accustomed to this process, adding cybersecurity features to the product initially means inconvenience and reduced productivity without perceivable benefits. Adapting development processes to take cybersecurity into account introduces challenges not present in engineering divisions so far. Strategies designed to deal with these challenges differ in the way in which added duties are assigned and cybersecurity topics are integrated into the already existing process steps. Cybersecurity requirements often clash with existing system requirements or established development methods, leading to low acceptance among developers, and introducing the need to have clear policies on how friction between cybersecurity and other fields is handled. A cybersecurity development approach is frequently perceived as introducing impediments, that bear the risk of cybersecurity measures receiving a lower priority to reduce inconvenience. Moreover, this leads to frustration among cybersecurity developers when their proposals are not accepted, and they feel their work is not appreciated. On the other hand, putting too much emphasis on cybersecurity leads to feature creep and makes the development unnecessarily complicated without producing appropriate results. It seems natural to orientate oneself by how safety topics are handled in the development process and adjust this to accommodate cybersecurity. It is, however, not clear in which way these added responsibilities should be assigned, as conflicts of interest occur when a single person must additionally take cybersecurity goals into account, which might be clashing with other project goals this person is responsible for. Ideally, cybersecurity aspects are considered and integrated into development processes not only to fulfill customer and legal requirements, but also to enable developers of functionalities not directly related to cybersecurity to produce better and more robust results as shortcuts are no longer easily possible.
Lenhart, Patric, Arndt, Paul, von Wedel, Jana, Beul, Christian, Weldert, Jan
Guidelines for Mobility Data Sharing Governance and ContractingMDC000012020044/8/2020
Digitally enabled mobility vehicles and services, including dockless bikesharing and electric scooter sharing, are generating and collecting a growing amount of mobility data. Mobility data holds great potential to support transportation officials and their efforts to manage the public right-of-way, but the unlimited distribution of mobility data carries untested risks to privacy and public trust. The Mobility Data Collaborative™ has identified the need to improve and coordinate understanding among all parties around foundational policy and legal issues to support mobility data sharing, including privacy and contracting. The guidelines are geared towards supporting a scalable mobility data sharing framework that aligns the interests of the public and private sectors while addressing privacy, transparency, data ownership, and consumer trust.
Mobility Data Collaborative
Seeing with Sound Next-Level Ultrasonic Sensors for Intelligent CarsTBMG-363744/1/2020
Even though ultrasound has been studied by scientists for many years, its capabilities in practical applications are yet to be fully harnessed.
Data Privacy in the Emerging Connected Mobility Services: Architecture, Use Cases, Privacy Risks, and Countermeasures11-02-01-000410/14/2019
The rapid development of connected and automated vehicle technologies together with cloud-based mobility services is transforming the transportation industry. As a result, huge amounts of consumer data are being collected and utilized to provide personalized mobility services. Using big data poses serious challenges to data privacy. To that end, the risks of privacy leakage are amplified by data aggregations from multiple sources and exchanging data with third-party service providers, in face of the recent advances in data analytics. This article provides a review of the connected vehicle landscape from case studies, system characteristics, and dataflows. It also identifies potential challenges and countermeasures.
Li, Huaxin, Ma, Di, Medjahed, Brahim, Kim, Yu Seung, Mitra, Pramita
Maintaining Data Protection and Security for the Connected CarTBMG-3527110/1/2019
The connected car has already become a reality. It is a subject not just electrifying customers and manufacturers but also security researchers and IT experts. And in a worst-case scenario, criminal hackers as well. For years, security experts have observed the fact that the desktop PC is not the only target of digital attacks anymore. A large part of the malware is now customized to hit mobile devices. It would be negligent to believe that this development would leave the connected car unmolested.
Autonomous Vehicle Engineering: September 201919AVEP099/5/2019
Editorial The new 'face' of privacy The Navigator No trust in AI systems without data protection Innovation Nation In the mobility space, Israel is rivaling Silicon Valley for smarts and start-ups - and beats it in chutzpah. Autonomy in your Face Biometric technology is deemed essential to ensuring AV driving safety and advancing the user experience-if privacy issues don't derail its deployment. About Face! To win acceptance, deployment of facial-recognition technology needs to fit within a picture-perfect consumer and legal framework that balances benefits with privacy protection. The Vehicle as Gaming Device Audi spin-off Holoride uses VR to turn the back seat into an entertainment platform. BlackBerry Tech Duo Sees Emergence of Vehicle-based Platforms Though likely to provide the OS of autonomy, BlackBerry also anticipates a larger shift to automobiles as software platforms. Improving Lidar - or Defeating It The buzz at Sensors Expo pitted lidar-tech optimism against the reality of an impending shakeout. 'Smart' in Ohio's Heartland With a 45-year history in vehicle testing, Ohio's Transportation Research Center launches a $45-million investment in the automated-vehicle future, becoming North America's largest dedicated AV test facility. Empathy to Elevate the User Experience Harman developers are striving to create human-machine interfaces oriented more towards user needs. ZF's Tech Portfolio is Ready for Level 4 Autonomy Well aware of the relentless hype that comes with automated driving development, ZF's autonomy boss knows cost will be crucial-and customer persuasion required. Trucking Without Truckers The challenges are myriad, but automated-trucking developer TUSimple believes the efficiencies of true depot-to-depot driverless hauling are too promising to ignore.
Editorial19AVEP09_019/1/2019
Although editor-in-chief Lindsay Brooke's cover story detailing the foundations of Israel's surging tech sector helps to explain the nation's expanding expertise in all things cyber-secure, much of this issue deals with security and privacy of another kind-the access we grant almost daily to all manner of digital “providers” of goods and services. The access we'll be expected to cede is in exchange for some of the promising new features of the looming connected-vehicle future.
Visnic, Bill
The Navigator19AVEP09_029/1/2019
No trust in AI systems without data protection When people surround themselves with microphones and cameras, others will be listening and watching. This reality shouldn't come as a surprise, but remarkably it does. The advent of automated driving will dramatically expand this activity. In fact, it is already creating controversy. At its core is the challenge of training so-called artificial intelligence systems. As human children, we learn in large part through the guidance we get from parents, teachers and other caregivers. AI systems are no different. They ingest data and “learn” from it. But just as children must be shown the difference between red and green, a circle and a square, or A and Z, machine learning systems need data that has been labeled.
Editorial19AVEP05_015/1/2019
AVs, data and ‘surveillance capitalism’ I finally hit my limit after seeing a Twitter post-from, almost paradoxically, a privacy engineer at Google-showing a taxi cab in Asia with a passenger-area screen announcing it was employing facial recognition “to deliver the most optimized content” to the rider. “The image data is discarded immediately,” the little tablet professed.
Visnic, Bill
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