Security
The expanding digital footprint of autonomous vehicles, coupled with the incorporation of artificial intelligence capabilities, has broadened the potential for cyber vulnerabilities. From an attacker's perspective, an autonomous driving system consists of three layers: Sensor Layer, Communication Layer, and Control Layer.
The sensor layer includes sensors that continuously monitor vehicle dynamics and the environment, but are vulnerable to eavesdropping, jamming, and spoofing attacks. The communication layer includes both near-field and far-field communications to enable communication between other edge sensors in the vicinity and remote edge data centers. This layer is vulnerable to ‘man-in-the-middle’ and Sybil attacks. The control layer at the top of the hierarchy enables autonomous driving system functions such as automating a vehicle's speed, braking, and steering. Attacks on the sensor and communication layers can propagate upward, compromising functionality and compromising the security of the control layer.63
Cybersecurity threats have been documented by gray or white hat hackers identifying cybersecurity threats in advanced driver assistance features available in passenger cars. Such as researchers from Keen Security Labs in China who in 2022 demonstrated a couple of exploits through a camera system in a Tesla Model S.64 Other researchers have exposed that DL models exploited in AVs to mimic human cognitive capabilities are not entirely secure and are highly vulnerable to attacks that might jeopardize the normal operation of AVs and provide unmodelled threats and unanticipated challenges to safety.
Addressing the rising potential of cyberattacks vehicles, security experts are shifting their focus towards proactive defense strategies. A cornerstone of this approach is the concept of "security by design." This philosophy emphasizes the integration of security features directly into the foundational design of technological systems, rather than treating them as secondary additions or retrofits. This proactive stance ensures that security considerations are woven into the fabric of the technology from its inception.
The most important security measures that can be implemented as part of security by design include encryption of data transmissions, authentication of communication participants, regular updating of software and firmware, and the use of intrusion detection prevention systems (IDPS).
Key advancements in IDPS for autonomous driving include:
● Machine Learning and AI Integration: Companies are utilizing machine learning and AI algorithms to enhance the accuracy and efficiency of intrusion detection. These systems can learn from historical data and adapt to new attack vectors, making them more resilient against evolving threats.
● Anomaly Detection Techniques: AV IDPS utilize sophisticated anomaly detection techniques to identify deviations from expected behavior. These techniques enable the system to detect novel attacks that might not match known attack patterns.
● Real-time Threat Analysis: IDPS for AVs operate in real-time, analyzing data streams from various sensors and vehicle components to detect and respond to threats as they occur.
● Collaborative Threat Intelligence: Some solutions incorporate shared threat intelligence databases, allowing vehicles to learn from each other's experiences and rapidly respond to emerging threats collectively.
This table provides an overview of some of the companies, their products, and how these are being utilized in the market, along with the types of users who are implementing these cybersecurity solutions in the autonomous vehicle sector.
Company | Products/Research | Users | Usage |
Argus Cyber Security | Argus Connectivity Protection, Argus Lifespan Protection | Automotive OEMs, Tier 1 suppliers | In various vehicle architectures including ECUs, telematics, infotainment systems |
Symantec (now part of Broadcom) | Symantec Integrated Cyber Defense Platform | Automotive manufacturers, suppliers | For comprehensive threat protection and management in automotive systems |
Harman | Harman's ECUSHIELD, TCUSHIELD | Automotive OEMs, telematics units | Securing in-vehicle and telematics systems against cyber threats |
Cisco | Cisco's automotive cybersecurity solutions | Connected vehicle manufacturers, infrastructure providers | Integrating cybersecurity in connected vehicle networks and infrastructure |
Securing AVs with Blockchain
Blockchain technology offers several ways to enhance security in connected Autonomous Vehicle (AV) services. Its unique characteristics make it a promising solution for some of the key challenges in this domain including:
Data Integrity and Traceability: Blockchain's inherent property of immutability ensures that once data is recorded, it cannot be altered without detection. Such data might include travel logs, sensor readings, or maintenance records. This traceability is essential for diagnosing issues, resolving liability questions in accidents, and preventing tampering.
Secure Communication: Blockchain can facilitate secure, decentralized communication between vehicles and infrastructure (V2X). By using blockchain's distributed ledger technology, AVs can validate and trust messages received from other vehicles or infrastructure without needing a central authority. This is particularly useful for preventing spoofing attacks where malicious entities might send false information to AVs.
Decentralized Operations: Unlike traditional centralized networks, blockchain operates on a decentralized network. This decentralization makes the system more resilient to cyberattacks, as there is no single point of failure. In the context of AVs, this could mean a more robust network for vehicle communication and coordination, less susceptible to large-scale attacks.
Identity Management and Authentication: Blockchain can be used to securely manage digital identities in the AV ecosystem. By using cryptographic keys for identity verification, it ensures that only authorized devices, vehicles, and infrastructure can communicate with each other. This can prevent unauthorized access and control of vehicle systems.
Smart Contracts for Automated Transactions: AVs can use blockchain-based smart contracts for automated, secure, and transparent transactions. This is particularly relevant for services like automated toll payments, parking fees, or even peer-to-peer energy transactions in the case of electric AVs.
Supply Chain Transparency: Blockchain can also enhance the security of the AV supply chain. By tracking the production, shipment, and installation of vehicle parts, blockchain can ensure authenticity and prevent counterfeit parts from being used, which could be a security risk.
Data Sharing and Privacy: Blockchain enables secure and selective data sharing. AVs generate vast amounts of data, and blockchain can facilitate the sharing of this data with third parties (like traffic management systems or other vehicles) in a way that preserves user privacy and data security.
Since 2020, numerous research papers have made significant contributions to the field of intelligent vehicle (IV) communication by harnessing blockchain technology, each with distinct areas of focus. Some studies have concentrated on establishing IV communication systems that place a high premium on security and reliability.
Diverse research directions within the domain of human safety and the aftermath of accidents have also been explored. One example is a reward-based system underpinned by crypto IV-TP, emphasizing the maintenance of unambiguous accident records. Furthermore, we can see innovative Multi-Agent AIM (MAAIM) systems, which adeptly manages the safe navigation of vehicles through intersections using V2I/I2V communication bolstered by blockchain technology.
Another line of research focuses on the secure real-time exchange of information among connected and autonomous vehicles. This endeavor is critical, particularly in light of emerging cyber threats. Cyberattacks, such as Denial-of-Service (DoS) attacks, can pose a substantial challenge to AV systems. These attacks may involve flooding the system with spurious requests, jeopardizing its functioning. Furthermore, it improves the overall security of IoT devices and positively impacts both the performance and scalability of AV services.
Lastly, localized Peer-to-Peer (P2P) electricity trading models have also been designed for Plug-in Hybrid Electric Vehicles (PHEVs) operating within smart grids. This model not only seeks to optimize costs but also enhances trustability and social welfare. By implementing an iterative double auction mechanism in localized P2P electricity trading systems, auctioneers are able to bid prices, ensuring transaction security, privacy protection, user satisfaction, and cost minimization or the attainment of the best prices. This multifaceted body of research underscores the diverse array of challenges and opportunities in the burgeoning field of IV communication within the context of blockchain technology.65
Companies Developing Security Solutions for AVs
ETAS
ETAS, in response to the increasing connectivity and automation of vehicles, has developed the ESCRYPT Intrusion Detection and Prevention Solution (IDPS) for connected fleets. This solution aims to monitor incidents and risks throughout the entire life cycle of vehicle fleets, complying with regulations such as UN Regulation 155 and ISO/SAE 21434. The ESCRYPT IDPS offers a holistic approach, ensuring continuous security improvements, permanent monitoring, and incident response.
The components of this end-to-end solution include the ESCRYPT Intrusion Detection Systems, Automotive Firewall (ESCRYPT CycurGATE), Threat Detection and Threat Intelligence (ESCRYPT Threat), and the monitoring backend product ESCRYPT CycurGUARD. Additionally, ETAS provides a Vehicle Security Operations Center (SOC) as a managed security service, integrating IT security expertise with automotive cybersecurity know-how to address the evolving threat landscape. The benefits of this solution include tailored one-stop delivery for vehicle fleets, operational excellence, global coverage, and openness to various in-vehicle Intrusion Detection Systems.6666
C2A Security
C2A Security specializes in securing in-vehicle communication and diagnostics systems, offering solutions that prevent unauthorized access and mitigate cyber risks in AVs. C2A Security delivers automated cybersecurity solutions that empower the evolution of connected, autonomous, and electric mobility. At the heart of C2A Security's offerings is their premier product, EVSec, a DevSecOps platform. This innovative solution equips automotive companies to maintain their competitive edge and enhance customer value in the era of software-defined vehicles. EVSec covers the full security lifecycle, spanning from development to operations and back. By employing EVSec, C2A's clientele gains access to effective and streamlined cybersecurity processes, enabling the efficient management of software on a large scale. This approach not only addresses the scarcity of cybersecurity experts but also ensures compliance with emerging regulations through automated means.67
It is important to emphasize that Valeo and C2A Security have formed a strategic collaboration to strengthen cybersecurity in Valeo's products, addressing the evolving landscape of software-defined vehicles and emerging automotive cyber regulations. The partnership aims to address the demand for efficient and streamlined cybersecurity solutions in the industry. C2A Security's expertise in automated cybersecurity is set to empower Valeo to implement advanced security measures while fostering innovation.68
Karamba Security
In May 2023, Karamba Security secured a production agreement for its XGuard Host Intrusion Detection and Prevention software. XGuard, with its continuous runtime integrity checks, intrusion detection, prevention capabilities, and reporting to the OEM's security operations center, addresses the growing emphasis on cybersecurity readiness among OEMs in line with UN R155 and emerging Chinese automotive cybersecurity regulations.
Praised for its in-depth security, simple integration, and minimal performance impact, Karamba's solution ensures compliance with regulations and enhances the security posture of vehicles. Additionally, Karamba provides deterministic and always-on security solutions for autonomous vehicles, utilizing Automotive Control Flow Integrity (CFI) to prevent cyberattacks without compromising performance. XGuard and SafeCAN offer comprehensive protection against external and in-vehicle network attacks, seamlessly integrating security into the ECU image build.
Key technical features include embedded XGuard agents with negligible performance overhead, unsupervised machine learning for anomaly detection, and compliance with ISO21434 and UNECE R155 cybersecurity standards. This approach ensures a self-defending vehicle with minimal performance impact.
Leadership Interviews
Interview with Mouser
Interview with Murata
Interview with MacroFab
Interview with Nexperia
Interview with SAE International
Interview with Autoware Foundation
Interview with NVIDIA
References
1. SceneScan Pro User Story: 3D Stereo Vision Cameras for Autonomous Racing Cars. https://nerian.com/news/scenescan-pro-user-story-3d-stereo-vision-cameras-for-autonomous-racing-cars/ (2022).
2. Foresight. 3D Stereo Vision for the Autonomous Vehicle Industry. Foresight https://www.foresightauto.com/3d-stereo-vision-for-the-autonomous-vehicle-industry/ (2023).
3. Bhadoriya, A. S., Vegamoor, V. & Rathinam, S. Vehicle Detection and Tracking Using Thermal Cameras in Adverse Visibility Conditions. Sensors 22, (2022).
4. Computer vision challenges in autonomous vehicles: The future of AI. https://www.superannotate.com/blog/computer-vision-in-autonomous-vehicles.
5. O’Neill, M. A Visionary Leap: Enhancing Computer Vision for Autonomous Vehicles and Cyborgs. SciTechDaily https://scitechdaily.com/a-visionary-leap-enhancing-computer-vision-for-autonomous-vehicles-and-cyborgs/ (2023).
6. AI-Enhanced night vision: A breakthrough for autonomous cars. IO https://innovationorigins.com/en/ai-enhanced-night-vision-a-breakthrough-for-autonomous-cars/ (2023).
7. Cvijetic, N. Panoptic Segmentation Helps Autonomous Vehicles See Outside the Box. NVIDIA Blog https://blogs.nvidia.com/blog/2019/10/23/drive-labs-panoptic-segmentation/ (2019).
8. Bridging to the Autonomous Future with Mobileye SuperVisionTM. Mobileye https://www.mobileye.com/blog/mobileye-supervision-bridge-to-consumer-autonomous-vehicles/.
9. LIDAR Sensor Integration in Autonomous Vehicles Platforms. https://www.mrlcg.com https://www.mrlcg.com/latest-media/lidar-sensor-integration-in-autonomous-vehicles-platforms-300849/ (2023).
10. Innovations of Autonomous Vehicles: Solid-State LiDAR Sensors. EqualOcean https://equalocean.com/analysis/2022031917148.
11. Home. Aeva https://www.aeva.com/ (2023).
12. Rahn, S. Technology. Blickfeld https://www.blickfeld.com/technology/ (2021).
13. Velodyne Lidar lanza un centro de diseño en Bangalore. https://www.businesswire.com/news/home/20210624006043/es/ (2021).
14. Innoviz Technologies. Innoviz Technologies to Present at Upcoming Investor Conferences. The Victoria Advocate https://www.victoriaadvocate.com/innoviz-technologies-to-present-at-upcoming-investor-conferences/article_69c829b2-d474-52b0-825b-7f3591907377.html (2023).
15. Luminar. Luminar (Nasdaq: LAZR). Luminar https://www.luminartech.com/.
16. Home. Aeva https://www.aeva.com/ (2023).
17. Quanergy filing for bankruptcy, going up for sale. Security Info Watch https://www.securityinfowatch.com/perimeter-security/physical-hardening/perimeter-security-sensors/press-release/21290282/quanergy-quanergy-filing-for-bankruptcy-going-up-for-sale (2022).
18. Quanergy Announces Industry-first 3D LiDAR Movement-Based False Alarm Reduction Solution. Quanergy Solutions, Inc. | LiDAR Sensors and Smart Perception Solutions https://quanergy.com/pressrel/quanergy-announces-industry-first-3d-lidar-movement-based-false-alarm-reduction-solution/.
19. [No title]. https://static.mobileye.com/website/corporate/media/radar-lidar-fact-sheet.pdf.
20. Nellis, S. Mobileye looks to build its own lidar to drive down self-driving costs. Reuters (2020).
21. High Resolution 3D Flash LiDARTM. Continental Automotive http://www.continental-automotive.com/en-gl/Passenger-Cars/Autonomous-Mobility/Enablers/Lidars/3D-Flash-Lidar.
22. Continental Aftermarket. http://www.continental-aftermarket.com/us-en/press/press-releases/2021/2021-11-08-continental-releases-hfl110-3d-flash-lidar-to-series-production.
23. Carman, A. Blickfeld Unveils First Smart LiDAR Sensor with Built-in Software Stack. All About Circuits https://www.allaboutcircuits.com/news/blickfeld-unveils-first-smart-lidar-sensor-with-built-in-software-stack/ (2022).
24. Blickfeld. Blickfeld launches Qb2; the world’s first Smart LiDAR sensor, enabling capturing and processing of 3D. Blickfeld https://www.blickfeld.com/blickfeld-launches-qb2/ (2022).
25. Difference between solid state radar and magnetron. https://lidarradar.com/info/difference-between-solid-state-radar-and-magnetron.
26. Hsu, W.-T. & Lin, S.-L. Using FMCW in Autonomous Cars to Accurately Estimate the Distance of the Preceding Vehicle. Int. J. Automot. Technol. Manage. 23, 1755–1762 (2023).
27. Global, B. Bosch Research Blog. Bosch Global https://www.bosch.com/stories/synthetic-aperture-radar/ (2022).
28. Manzoni, M. et al. Automotive SAR imaging: potentials, challenges, and performances. International Journal of Microwave and Wireless Technologies 1–10 (2023).
29. Website. https://www.researchgate.net/publication/334633392_Synthetic_Aperture_Radar_Towards_Automotive_Applications.
30. [No title]. https://arxiv.org/pdf/2306.09784.pdf.
31. Website. https://omdia.tech.informa.com/blogs/2023/sensors-for-safe-autonomous-driving.
32. IEEE Xplore Full-Text PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9528970.
33. Cvijetic, N. How AI Improves Radar Perception for Autonomous Vehicles. NVIDIA Blog https://blogs.nvidia.com/blog/2021/04/28/drive-labs-ai-radar-perception-autonomous-vehicles/ (2021).
34. Autonomous Vehicles. Navtech Radar https://navtechradar.com/explore/autonomous-vehicles/ (2021).
35. The Importance of Imaging Radar. http://www.nxp.com/company/blog/the-importance-of-imaging-radar:BL-THE-IMPORTANCE-OF-IMAGING-RADAR.
36. eVehicle Technology. New 4D Imaging Radar Sensor to Revolutionise Automotive Safety. eVehicle Technology https://www.evehicletechnology.com/news/4d-imaging-radar-sensor-to-revolutionise-automotive-safety/ (2020).
37. Vayyar. Vayyar Becomes First And Only Company In World To Offer Full-cabin Monitoring With One Radar-on-chip. PR Newswire https://www.prnewswire.com/news-releases/vayyar-becomes-first-and-only-company-in-world-to-offer-full-cabin-monitoring-with-one-radar-on-chip-301251217.html (2021).
38. Maanpää, J. et al. Multimodal End-to-End Learning for Autonomous Steering in Adverse Road and Weather Conditions. https://ieeexplore.ieee.org/document/9413109.
39. Multi-modality 3D object detection in autonomous driving: A review. Neurocomputing 553, 126587 (2023).
40. [No title]. https://arxiv.org/pdf/2002.00444.pdf.
41. Ravi Kiran, B. et al. Deep Reinforcement Learning for Autonomous Driving: A Survey. https://ieeexplore.ieee.org/document/9351818.
42. Wiggers, K. Uber claims its AI enables driverless cars to predict traffic movement with high accuracy. VentureBeat https://venturebeat.com/ai/uber-claims-its-ai-enables-driverless-cars-to-predict-traffic-movement-with-high-accuracy/ (2020).
43. Edge AI Computing Advancements Driving Autonomous Vehicle Potential. Global Semiconductor Alliance https://www.gsaglobal.org/forums/edge-ai-computing-advancements-driving-autonomous-vehicle-potential/ (2021).
44. Snapdragon Ride SDK: a premium platform for developing customizable ADAS applications. https://www.qualcomm.com/news/onq/2022/01/snapdragon-ride-sdk-premium-solution-developing-customizable-adas-and-autonomous.
45. Edge AI Computing for Autonomous Driving System - Lanner Electronics. https://www.lannerinc.com/applications/transportation/edge-ai-computing-for-autonomous-driving-system.
46. AI Accelerator Chips Overview and Comparison. HardwareBee https://hardwarebee.com/ai-accelerator-chips-overview-and-comparison/ (2023).
47. Lardinois, F. Google Cloud announces the 5th generation of its custom TPUs. TechCrunch https://techcrunch.com/2023/08/29/google-cloud-announces-the-5th-generation-of-its-custom-tpus/ (2023).
48. Francisco, A. Intel Brings New Chips, Autonomous Driving, and Artificial Intelligence to CES 2020. Showmetech https://www.showmetech.com.br/en/intel-brings-chips-and-news-to-ces-2020/ (2020).
49. Hajela, R. Looking at 11th Generation Intel® Processor Performance on Intel® DevCloud for Edge Workloads. OpenVINO-toolkit https://medium.com/openvino-toolkit/looking-at-11th-generation-intel-processor-performance-on-intel-devcloud-for-the-edge-213a413fb5e1 (2022).
50. Riley, D. Intel makes a splash at CES with AI, autonomous driving tech and Tiger Lake chips. SiliconANGLE https://siliconangle.com/2020/01/06/intel-makes-splash-ces-autonomous-driving-tech-ai-tiger-lake/ (2020).
51. Self-Driving Cars Technology & Solutions from NVIDIA Automotive. NVIDIA https://www.nvidia.com/en-us/self-driving-cars/.
52. Volvo Cars, Zoox, SAIC and More Join Growing Range of Autonomous Vehicle Makers Using New NVIDIA DRIVE Solutions. NVIDIA Newsroom http://nvidianews.nvidia.com/news/volvo-cars-zoox-saic-and-more-join-growing-range-of-autonomous-vehicle-makers-using-new-nvidia-drive-solutions.
53. Korosec, K. Qualcomm unveils its Snapdragon Ride platform for all levels of automated driving. TechCrunch https://techcrunch.com/2020/01/06/qualcomm-unveils-its-snapdragon-ride-platform-for-all-levels-of-automated-driving/ (2020).
54. Website. https://europe.autonews.com/automakers/vw-use-qualcomms-snapdragon-ride-platform-autonomous-driving-push.
55. Automotive RTOS. https://blackberry.qnx.com/en/ultimate-guides/software-defined-vehicle/automotive-rtos.
56. Saxena, A. Understanding AUTOSAR and its Applications in the Automotive Industry. https://www.einfochips.com/blog/autosar-in-automotive-industry/ (2020).
57. Apex.AI. Apex.AI https://www.apex.ai/blog.
58. Cognata. Cognata Autonomous and ADAS Simulation https://www.cognata.com/ (2022).
59. Ford China deploys C-V2X service in Changchun, Jilin Province. https://autonews.gasgoo.com/m/70024138.html.
60. Qualcomm Introduces Car-to-Cloud Service for Over-the-Air Vehicle Updates and On-Demand Services & Features. https://www.qualcomm.com/news/releases/2020/01/qualcomm-introduces-car-cloud-service-over-air-vehicle-updates-and-demand.
61. Vehicle Telematics, Geofencing & Fleet Tracking Systems for the Connected Car. (2006).
62. Cisco 5G Network Architecture. (2020).
63. Infoshare Systems, Inc. How cyber security threats are impacting the automotive industry. https://www.linkedin.com/pulse/how-cyber-security-threats-impacting-automotive-industry-1f (1685456083000).
64. Website. https://www.sciencedirect.com/science/ article/pii/S209580991930503X.
65. [No title]. https://iopscience.iop.org/article/10.1088/1742-6596/1694/1/012024/pdf.
66. ESCRYPT Intrusion detection and prevention solution. https://www.etas.com/en/products/intrusion-detection-and-prevention-solution.php.
67. TRANSFORMING SECURITY FROM A LIMITATION TO A BUSINESS VALUE MULTIPLIER. C2A Security - the Only Mobility Centric DevSecOps Platform https://c2a-sec.com/ (2022).
68. C2A. C2A Security Partners with Valeo to Enhance Cybersecurity for the Software Defined Vehicle. PR Newswire https://www.prnewswire.com/news-releases/c2a-security-partners-with-valeo-to-enhance-cybersecurity-for-the-software-defined-vehicle-301713633.html (2023).
69. Dave, P. Dashcam Footage Shows Driverless Cars Clogging San Francisco. WIRED https://www.wired.com/story/dashcam-footage-shows-driverless-cars-cruise-waymo-clogging-san-francisco/ (2023).
70. Waymo Driver. Waymo https://waymo.com/waymo-driver/.
71. How Autonomous Vehicles Work. https://ltad.com/about/how-autonomous-vehicles-work.html.
72. Merchant, B. Column: Self-driving Waymo cars might worsen L.A. traffic. Los Angeles Times (2023).
73. Waymo Driver. Waymo https://waymo.com/waymo-driver/.
74. Rangaiah, M. How Waymo is using AI for autonomous driving? https://www.analyticssteps.com/blogs/how-waymo-using-ai-autonomous-driving.
75. Website. https://www.uber.com/blog/autonomous-vehicle-safety-guidelines/.
76. Reuters. Uber partners with Alphabet’s Waymo to offer driverless rides. Reuters (2023).
77. Jennewine, T. 6 Ways Tesla’s Autonomous Driving Technology Is Evolving. The Motley Fool https://www.fool.com/investing/2022/01/27/6-ways-teslas-autonomous-driving-technology-is-evo/ (2022).
78. Tesla Vision Update: Replacing Ultrasonic Sensors with Tesla Vision. Tesla https://www.tesla.com/support/transitioning-tesla-vision.
79. Moon, S. How Tesla made autonomous possible without LIDAR. Datahunt - Quality Data with Quality AI https://www.thedatahunt.com/en-insight/how-tesla-autonomous-driving-possible-without-lidar (2023).
80. Autopilot. https://www.tesla.com/autopilot.
81. Fox, E. Tesla 2020.4.11 Software OTA Update To Boost Ranges Of Model S/X. TESMANIAN https://www.tesmanian.com/blogs/tesmanian-blog/tesla-2020-4-11-software-ota-update-to-boost-ranges-of-model-s-x (2020).
82. Cardello, S. What Are Over-the-Air Updates For Cars? MUO https://www.makeuseof.com/what-are-over-the-air-updates-for-cars/ (2022).
83. Hawkins, A. J. Here’s our first full look at the Cadillac Celestiq ultra-luxury electric sedan. The Verge https://www.theverge.com/23273937/gm-cadillac-celestiq-ev-sedan-photos-specs-reveal (2022).
84. [No title]. https://www.volvocars.com/intl/news/technology/Your-Volvo-car-will-understand-you/.
85. Pollock, D. General Motors Applies For Decentralized Blockchain Map Patent. Forbes https://www.forbes.com/sites/darrynpollock/2020/04/03/general-motors-applies-for-decentralized-blockchain-map-patent/ (2020).
86. Schoppa, C. Top Autonomous Vehicles Companies to Watch in 2023. AI Time Journal - Artificial Intelligence, Automation, Work and Business https://aitimejournal.com/autonomous-vehicles-companies-to-watch/ (2022).
87. Ammann, D. The Cruise Origin Story - Cruise - Medium. Cruise https://medium.com/cruise/the-cruise-origin-story-b6e9ad4b47e5 (2020).
88. Vara, V. Leading vehicle manufacturing companies in the autonomous vehicles theme. Just Auto http://www.just-auto.com/data-insights/top-ranked-vehicle-manufacturing-companies-in-autonomous-vehicles/ (2022).
89. Autonomous Vehicle Technology. https://getcruise.com/technology/.
90. Pavlik, T. When Volvo will have its first autonomous vehicle might surprise you. MotorBiscuit https://www.motorbiscuit.com/volvo-first-autonomous-vehicle-might-surprise/ (2022).
91. Dyer, N. Volvo’s Ride Pilot autonomous driving tech allows unsupervised driving. CARHP https://www.carhp.com/news/volvo-s-ride-pilot-autonomous-driving-tech-allows-unsupervised-driving (2023).
92. Rodríguez, J., Jr. Volvo EX90 comes with self-driving hardware, accessible by a subscription. Jalopnik https://jalopnik.com/volvo-ex90-subscription-based-self-driving-tech-1850197959 (2023).
93. Kirby, C. & Flear, K. Volvo’s First OTA Update: More Range for Some Models. https://getjerry.com/insights/volvos-first-ota-update-range-models (2022).
