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To meet legal requirements vehicle manufacturers have to use a standard drive-by noise acceleration test conforming to relatively easily specified procedures (gear, approach speed etc). However, due to the transient conditions occurring during the test, predicting maximum drive-by noise levels from the contributions of vehicle systems is difficult. As manufacturers need to identify early in the design of a vehicle those available systems which will ensure legal requirements are met, a technique is required that can predict the contribution of each system. The technique has to be able to accept system target & CAE data as well as test data in order that it can be used in all stages of a vehicle program.

The advancement towards development of autonomy follows either the bottom-up approach of gradually improving and expanding existing Advanced Driver Assist Systems (ADAS) technology where the driver is present in the control loop or the top-down approach of directly developing Autonomous Vehicles (AV) hardware and software using alternative approaches without the driver present in the control loop. Most ADAS systems today fall under the classification of SAE Level 1 which is also referred to as the driver assistance level. The progression from SAE Level 1 to SAE Level 2 or partial automation involves the critical task of merging autonomous lateral control and autonomous longitudinal control such that the tasks of steering and acceleration/deceleration are not required to be handled by the driver under certain conditions [1].

The automotive industry is heading towards the path of autonomy with the development of autonomous vehicles. An autonomous vehicle consists of two main components. The first is the software which is responsible for the decision-making capabilities of the system. The second is the hardware which encompasses all aspects of the physical vehicle which are responsible for vehicle motion such as the engine, brakes and steering subsystems along with their corresponding controls. This component forms the basis of the autonomous vehicle platform. For SAE Level 4 autonomous vehicles, where an automated driving system is responsible for all the dynamics driving tasks including the fallback driving performance in case of system faults, redundant mechanical systems and controls are required as part of the autonomous vehicle platform since the driver is completely out of the loop with respect to driving.

Level 2 (L2) partially automated vehicle systems require the driver to continuously monitor the driving environment and be prepared to take control immediately if necessary. One of the main challenges facing developers of these systems is how to ensure that drivers understand their role and stay alert as the systems require. With little real world data, it has been difficult to understand user attitudes and behaviors toward the implementation and use of partially automated vehicles. At the time of this study, Tesla was one of the few OEMs with a partially automated vehicle feature available on the market; Autopilot. In order to understand how customers interact with a partially automated vehicle, a study was conducted to observe people driving their own Tesla vehicles while autopilot was engaged. Sixteen Tesla owners (14 males and 2 females) between ages 25 to 60 had their vehicles instrumented with video/audio data collection systems for three consecutive days.

With the recent advancements in sensing and processing capabilities of consumer mobile devices (e.g., smartphone, tablet, etc.), they are becoming attractive choices for pervasive computing applications. Always-on monitoring of human movement patterns is one of those applications that has gained a lot of importance in the field of mobility and transportation research. Automatic detection of the current transportation mode (e.g., walking, biking, riding a shuttle, etc.) of a consumer using data from their smartphone sensors enables delivering of a number of customized services for multi-modal journey planning. Most accurate models for automatic mode detection are trained with supervised learning algorithms. In order to achieve high accuracy, the training datasets need to be sufficiently large, diverse, and correctly labeled.

Road Load Data is an important source of information for quality improvements. Vehicle and component load information, such as driver behaviour and other characteristics of vehicle use in real world conditions, are the basis for many engineering tasks, including fuel economy and life-time optimization. This new approach in road load data acquisition is based on the increasing existence of data bus networks in modern vehicles, as well as further improvements in data acquisition technologies. The applications of which alllow smart, inobtrusive solutions and, increasingly, the collection of real world customer data. This methodology of data collection leads to a significant alteration of the company-wide data base, relevant for future engineering efforts. The growing share of real world customer data will result in a more optimized and customer-orientated range of solutions, for all areas of vehicle engineering.

The demand for seating comfort is growing - in cars as well as trucks and other commercial vehicles. This is expected as the seat is the largest surface area of the vehicle that is in contact with the occupant. While it is predominantly luxury cars that have been equipped with climate controlled seats, there is now a clear trend toward this feature becoming available in mid-range and compact cars. The main purpose of climate controlled seats is to create an agreeable microclimate that keeps the driver comfortable. It also reduces the “stickiness” feeling which is reported by perspiring occupants on leather-covered seats. As part of the seat design process, a physical test is performed to record and evaluate the life cycle and the performance at ambient and extreme temperatures for the climate controlled seats as well as their components. The test calls for occupied and unoccupied seats at several ambient temperatures.

One of the ways to reduce city congestion is to balance the traffic flow on the road network and maximally utilize all road capacities. There are examples showing that, if the drivers are not competitive but cooperative, the road network usage efficiency and the traffic conditions can be improved. This motivates the idea of designing a cooperative routing algorithm to benefit most vehicles on the road. This paper presents a semi-cooperative social routing algorithm for large transportation network with predictive traffic density information. The goal is to integrate a cooperative scheme into the individual routing and achieve short traveling time not only for the traveler itself, but also for all vehicles in the road network. The most important concept of this algorithm is that the route is generated with the awareness of the total travel time added to all other vehicles on the road due to the increased congestion.

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.

As automakers begin to design the autonomous vehicle (AV) for the first time, they must reconsider customer interaction with the Automatic Speech Recognition (ASR) system carried over from the traditional vehicle. Within an AV, the voice-to-ASR system needs to be capable of serving a customer located in any seat of the car. These shifts in focus require changes to the microphone selection and placement to serve the entire vehicle. Further complicating the scenario are new sources of noise that are specific to the AV that enable autonomous operation. Hardware mounted on the roof that are used to support cameras and LIDAR sensors, and mechanisms meant to keep that hardware clean and functioning, add even further noise contamination that can pollute the voice interaction. In this paper, we discuss the ramifications of picking up the intended customer’s voice when they are no longer bound to the traditional front left “driver’s” seat.

This paper demonstrates a solution to the security problem for automotive wireless powertrain networking. That is, the security for wireless automotive networking requires a localization function before we allow a node to join the network. We explain why for powertrain wireless networking, this ability of identifying the precise location of a communicating wireless node is critical. In this paper, we explore existing methods that others have used to implement localization for wireless networking. Then, we apply machine-learning techniques to a dataset that has localization information associated with received signal strength indication. We reveal insights provided by our dataset though an exploration with statistics and visualization. We then present our problem in terms of pattern recognition via multiple techniques, including Naïve Bayes Classifier and Artificial Neural Networks.

Electrified powertrains respond to driver demand for vehicle acceleration by producing power through either the electric drive system or an on-board combustion engine or both. In Plug-In Hybrid Vehicles (PHEVs), the powertrain provides the purest form of transportation when responding to driver demand through the electric drive system. We develop a method to size the electric drive system in PHEVs to provide zero emission driving in densely populated urban regions. We use real world data from Europe and calculate instantaneous wheel power during trips. Ray tracing is used to identify the regions where trips occur and the population density of these regions is obtained from an open source dataset published by Eurostat. Regions are categorized by their population density into urban and extra-urban regions. Real world data from these regions is analyzed to determine the wheel power required in urban and extra-urban settings.

Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences.

Adaptive Cruise Control (ACC) is becoming a common feature in modern day vehicles with the advancement of Advanced Driver Assist Systems (ADAS). Simultaneously, Hardware-in-the-Loop (HIL) simulation has emerged as a major component of the automotive product development cycle as it can accelerate product development and validation by supplementing in-vehicle testing. Specifically, HIL simulation has become an integral part of the controls development and validation V-cycles by enabling rapid prototyping of control software for Electronic Control Units (ECUs). Traditionally, ACC algorithms have been validated on a system or subsystem HIL bench with the ACC ECU in the loop such that the HIL bench acts as the host or trailing vehicle with the target or preceding vehicle usually simulated using as an object that follows a pre-defined motion profile.

The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.