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Controller Area Network (CAN), the de facto standard for in-vehicle networks, has insufficient security features and thus is inherently vulnerable to various attacks. To protect CAN bus from attacks, intrusion detection systems (IDSs) based on advanced deep learning methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have been proposed to detect intrusions. However, those models generally introduce high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory requests, we exploit the use of Binarized Neural Network (BNN) and hardware-based acceleration for intrusion detection in in-vehicle networks. As BNN uses binary values for activations and weights rather than full precision values, it usually results in faster computation, smaller memory cost, and lower energy consumption than full precision models.

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.

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.

This study was conducted to develop and validate a multidimensional measure of shift quality as perceived by drivers during kick-down shift events for automatic transmission vehicles. As part of the first study, a survey was conducted among common drivers to identify primary factors used to describe subjective gear-shifting qualities. A factor analysis on the survey data revealed four semantic subdimensions. These subdimensions include responsiveness, smoothness, unperceivable, and strength. Based on the four descriptive terms, a measure with semantic scales on each subdimension was developed and used in an experiment as the second study. Twelve participants drove and evaluated five vehicles with different gear shifting patterns. Participants were asked to make kick-down events with two different driving intentions (mild vs. sporty) across three different speeds on actual roadway (local streets and highway).

This paper describes the design and application of a business simulation to help train employees about the new business model and culture that for an automotive supplier company that designs connected vehicle and other advanced electronic products for the automotive industry. The simulation, called SIM-i-TRI, is a three to four day collaborative learning activity that simulates the executive, administrative, engineering, manufacturing, and marketing functions in three divisions of a manufacturer that supplies parts and systems to customers in industries similar to the automotive industry. It was originally designed to support the new employee orientation at the Tier 1 supplier and to provide the participants a safe environment to practice the lessons from the orientation. The simulation has been used several times a month in the US, England, and Germany for over four years.

This paper presents results of a three-phase research project aimed at understanding how future automotive interior materials should be selected or designed to satisfy the needs of the customers. The first project phase involved development of 22 five-point semantic differential scales to measure visual, visual-tactile, and evaluative characteristics of the materials. Some examples of the adjective pairs used to create the semantic differential scales to measure the perceptual characteristics of the material are: a) Visual: Light vs. Dark, Flat vs. Shiny, etc., b) Visual-Tactile: Smooth vs. Rough, Slippery vs. Sticky, Compressive vs. Non-Compressive, Textured vs. Non-Textured, etc., c) Evaluative (overall perception): Dislike vs. Like, Fake vs. Genuine, Cheap vs. Expensive, etc. In the second phase, 12 younger and 12 older drivers were asked to evaluate a number of different automotive interior materials by using the 22 semantic differential scales.

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.

The automotive interior suppliers are challenged to develop materials, that not only perform functionally, but also provide the right combination sensory experience (e.g. visual appeal, tactile feeling) and brand differentiation at very competitive costs. Therefore, the objective of this research presented in this paper is to develop a methodology that can be used to measure customer perception of interior materials and to come up with a unique system for assessing value of different interior materials. The overall methodology involves the application of a number of psychophysical measurement methods (e.g. Semantic Differential Scaling) and statistical methods to assess: 1) overall customer perceived quality of materials, 2) elements (or attributes) of perception, and 3) value of materials from OEM's viewpoint in terms of the measurement of perception of quality divided by a measure of cost.

This paper presents a real-time computer system for the control of refrigerant flow in an automotive air conditioning system. This is an experimental system used to investigate the potential advantages of electronic flow control over conventional flow control (using an orifice tube or thermal expansion valve). Two features of this system are presented. First, the system organization is described. Second, the control and interface software are presented. The emphasis is on the software. The system is organized as a closed loop control system. The inputs to the controller are measurements of the refrigerant system. In particular, thermocouples are used to measure the refrigerant temperature before and after the evaporator. The analog thermocouple signals are converted to digital form by an off-the-shelf, portable, data acquisition system (DAQ). Via a parallel port link, these digital measurements are transfered to a laptop computer.