Biblio

With self-driving cars making their way on to our roads, we ask not what it would take for them to gain acceptance among consumers, but what impact they may have on other drivers. How they will be perceived and whether they will be trusted will likely have a major effect on traffic flow and vehicular safety. This work first undertakes an exploratory factor analysis to validate a trust scale for human-robot interaction and shows how previously validated metrics and general trust theory support a more complete model of trust that has increased applicability in the driving domain. We experimentally test this expanded model in the context of human-automation interaction during simulated driving, revealing how using these dimensions uncovers significant biases within human-robot trust that may have particularly deleterious effects when it comes to sharing our future roads with automated vehicles.

Wear and tear are essential in establishing the market value of an asset. From shutter counters on DSLRs to odometers inside cars, specific counters, that encode the degree of wear, exist on most products. But malicious modification of the information that they report was always a concern. Our work explores a solution to this problem by using the blockchain technology, a layered encoding of product attributes and identity-based cryptography. Merging such technologies is essential since blockchains facilitate the construction of a distributed database that is resilient to adversarial modifications, while identity-based signatures set room for a more convenient way to check the correctness of the reported values based on the name of the product and pseudonym of the owner alone. Nonetheless, we reinforce security by using ownership cards deployed around NFC tokens. Since odometer fraud is still a major practical concern, we discuss a practical scenario centered on vehicles, but the framework can be easily extended to many other assets.

With the growing number of electronic features in cars and their connections to the cloud, smartphones, road-side equipment, and neighboring cars the need for effective cybersecurity is paramount. Beyond the concern of brand degradation, warranty fraud, and recalls, what keeps manufacturers up at night is the threat of malicious attacks which can affect the safety of vehicles on the road. Would any single protection technique provide the security needed over the long lifetime of a vehicle? We present a new methodology for automotive cybersecurity where the designs are made to withstand attacks in the future based on the concepts of high availability and malicious fault-tolerance through self-defending techniques. When a system has an intrusion, self-defending technologies work to contain the breach using integrity verification, self-healing, and fail-over techniques to keep the system running.