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Technical Paper

Driver Workload in an Autonomous Vehicle

As intelligent automated vehicle technologies evolve, there is a greater need to understand and define the role of the human user, whether completely hands-off (L5) or partly hands-on. At all levels of automation, the human occupant may feel anxious or ill-at-ease. This may reflect as higher stress/workload. The study in this paper further refines how perceived workload may be determined based on occupant physiological measures. Because of great variation in individual personalities, age, driving experiences, gender, etc., a generic model applicable to all could not be developed. Rather, individual workload models that used physiological and vehicle measures were developed.
Technical Paper

Secure and Privacy-Preserving Data Collection Mechanisms for Connected Vehicles

Nowadays, the automotive industry is experiencing the advent of unprecedented applications with connected devices, such as identifying safe users for insurance companies or assessing vehicle health. To enable such applications, driving behavior data are collected from vehicles and provided to third parties (e.g., insurance firms, car sharing businesses, healthcare providers). In the new wave of IoT (Internet of Things), driving statistics and users’ data generated from wearable devices can be exploited to better assess driving behaviors and construct driver models. We propose a framework for securely collecting data from multiple sources (e.g., vehicles and brought-in devices) and integrating them in the cloud to enable next-generation services with guaranteed user privacy protection.
Technical Paper

Sybil Attacks on Vehicular Ad-Hoc Networks

Security is a huge concern in VANETs (Vehicular Ad hoc NETworks) since the information being conveyed may affect life-or-death decisions. One of the security concerns is the Sybil Attack. This attack attempts to create multiple identities to disrupt or control the network. A malicious node utilizing the Sybil Attack in VANETs can disrupt the network in various ways. It can create a large number of Sybil nodes to intervene in message forwarding, potentially causing a massive pileup and great loss of life. A malicious node can also use the Sybil Attack to create illusions of traffic congestions, getting other drivers to take alternate routes and leaving a clear path for the malicious node to its destination. In this paper, we discuss several defense strategies for the Sybil Attack in VANETs.