scholarly journals Energy Impact of Connected and  Automated Vehicle Technologies (Final report)

2020 ◽  
Author(s):  
Huei Peng
Keyword(s):  
Final Report ◽  
Automated Vehicle ◽  
Energy Impact ◽  
Vehicle Technologies

Unsettled Topics Concerning the Impact of Quantum Technologies on Automotive Cybersecurity

2020 ◽  
Author(s):  
Joachim Taiber ◽  
Keyword(s):  
Quantum Computing ◽  
Quantum Cryptography ◽  
Research Report ◽  
Quantum Computers ◽  
Encrypted Data ◽  
Automated Vehicle ◽  
Vehicle Technologies ◽  
Post Quantum Cryptography ◽  
Vehicle Sensors ◽  
The Impact

Quantum computing is considered the “next big thing” when it comes to solving computational problems impossible to tackle using conventional computers. However, a major concern is that quantum computers could be used to crack current cryptographic schemes designed to withstand traditional cyberattacks. This threat also impacts future automated vehicles as they become embedded in a vehicle-to-everything (V2X) ecosystem. In this scenario, encrypted data is transmitted between a complex network of cloud-based data servers, vehicle-based data servers, and vehicle sensors and controllers. While the vehicle hardware ages, the software enabling V2X interactions will be updated multiple times. It is essential to make the V2X ecosystem quantum-safe through use of “post-quantum cryptography” as well other applicable quantum technologies. This SAE EDGE™ Research Report considers the following three areas to be unsettled questions in the V2X ecosystem: How soon will quantum computing pose a threat to connected and automated vehicle technologies? What steps and measures are needed to make a V2X ecosystem “quantum-safe?” What standardization is needed to ensure that quantum technologies do not pose an unacceptable risk from an automotive cybersecurity perspective?

scholarly journals Modelling Uptake Sensitivities of Connected and Automated Vehicle Technologies

2021 ◽  
Vol 10 (2) ◽  
pp. 88-106
Author(s):  
Gillian Harrison ◽  
Simon P. Shepherd ◽  
Haibo Chen
Keyword(s):  
Internet Of Things ◽  
Dynamic Modelling ◽  
Transport Systems ◽  
The Internet ◽  
Sensitivity Testing ◽  
Automated Vehicle ◽  
Vehicle Technologies ◽  
The Internet Of Things

Connected and automated vehicle (CAV) technologies and services are rapidly developing and have the potential to revolutionise the transport systems. However, like many innovations, the uptake pathways are uncertain. The focus of this article is on improving understanding of factors that may affect the uptake of highly and fully automated vehicles, with a particular interest in the role of the internet of things (IoT). Using system dynamic modelling, sensitivity testing towards vehicle attributes (e.g., comfort, safety, familiarity) is carried out and scenarios were developed to explore how CAV uptake can vary under different conditions based around the quality of IoT provision. Utility and poor IoT are found to have the biggest influence. Attention is then given to CAV ‘services' that are characterized by the attributes explored earlier in the paper, and it is found that they could contribute to a 20% increase in market share.

scholarly journals The autonomous vehicle social network: Analyzing tweets after a recent Tesla autopilot crash

2019 ◽  
Vol 63 (1) ◽  
pp. 2071-2075
Author(s):  
Jacelyn Jefferson ◽  
Anthony D. McDonald
Keyword(s):  
Social Media ◽  
Social Network ◽  
Autonomous Vehicle ◽  
Willingness To Accept ◽  
Automated Vehicle ◽  
The Social ◽  
Social Media Platforms ◽  
Vehicle Technology ◽  
Vehicle Technologies ◽  
Negative Sentiment

Automated vehicle technologies offer a potentially safer alternative than manually driven vehicles, but only if they are accepted and used appropriately. Social media platforms may offer an opportunity to assess peoples’ willingness to accept and use automated vehicle technology, but questions remain on the structure and content of the social media conversation. To answer these questions, we performed an analysis of tweets surrounding a recent Tesla Autopilot incident. Tweets were analyzed at three levels: term frequency, account tweet and retweet frequency, and sentiment. The most frequent terms of the conversation shifted from “amazon” and “startup” to “autopilot” and “vehicle” following the crash, however, the specific tweet content referenced an earlier event. A small portion of accounts were responsible for the majority of the tweets in the dataset, and were rarely retweeted. Positive and negative sentiment decreased following the crash, suggesting that a more complex sentiment analysis is needed to gauge changes in public opinion of automated vehicles.

scholarly journals Guest Editors’ Introduction: Multimodal Technologies and Interaction in the Era of Automated Driving

2019 ◽  
Vol 3 (2) ◽  
pp. 41
Author(s):  
Andreas Riener ◽  
Myounghoon Jeon
Keyword(s):  
User Needs ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Vehicle Technologies

Recent advancements in automated vehicle technologies pose numerous opportunities and challenges to support the diverse facets of user needs [...]

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