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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.

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.

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.

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.

There has been a growing concern about increasing vehicle-mile traveled (VMT) associated with deadhead trips for dynamic rideshare services, particularly with the emergence of Shared Autonomous Vehicle (SAV) services. Studies in the literature on repositioning strategies have been limited to synthetic or small-scale study areas. This study considers a large-scale computational experiment involving a New York City study area with a network of 16,782 nodes and 23,337 links with 662,455 potential travelers from the 2016 Yellow Taxi data. We investigate the potential to reduce VMT and deadhead miles for dynamic rideshare operations combined with vehicle repositioning strategies. Three repositioning strategies are evaluated: (1) Roaming around areas with higher pickup probabilities to maximize the chance of picking up passengers, (2) Staying at curb side after completing trips, and (3) Repositioning to depots to minimize deadhead trips.

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.

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.

In many fields of technology, examinations of the past can provide insights into the future. This paper reviews the last 20 years of automotive seat comfort development and research as chronicled by SAE’s session titled “Human Factors in Seating Comfort”. Records suggest that “Human Factors in Seating Comfort” has existed as a separate session at SAE’s World Congress since 1999. In that time there have been 148 unique contributions (131 publications). The history is fascinating because it reflects interests of the time that are driven by technology trends, customer wants and needs, and new theories. The list of contributors, in terms of authors and their affiliations, is also telling. It shows shifts in business models and strategies around collaboration. The paper ends with a discussion of what can be learned from this historical review and the major issues to be addressed. One of the more significant contributions of this paper is the reference list.

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.