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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.

A computer model solving the 1-D flow in a typical fuel injection system for direct-injection diesel engines is presented. A Bosch distributor - type VE pump connected to four Stanadyne pencil - type nozzles has been used to validate the computer model over a wide range of operating conditions. Validation of the developed computer code has been performed for eight representative test cases. The predicted values which were compared with the experimental ones include the pumping chamber pressure, the line pressure, the needle lift and the injection rate. Results using as input the measured pumping chamber pressure are also presented in order to identify the error in the injection rate signal attributed to the difference between the simulated and the experimental pumping chamber pressure. In addition, the total fuel injection quantity for pump speeds between 500 and 2000 rpm and lever positions between 20% to 100% was calculated and compared with measurements.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

The prevention of automotive releases of the unpleasant smelling hydrogen sulfide (H2S) is highly desirable. However, the ability to routinely test catalysts for dynamic H2S releases corresponding to the real world has traditionally proved difficult. The work herein identifies the key steps taken to produce a highly repeatable (overall relative standard deviation of typically less than 10%) procedure capable of replicating H2S releases from wide-open throttle (WOT) events. The testing utilized a chassis dynamometer to test a gasoline vehicle (fitted with one TWC system) over a specific transient drive cycle with H2S emissions detected using a chemical ionization mass spectrometer and an infra-red detection based system. The importance of the warm-up and catalyst preparation parts of the test are discussed, including statistical analysis. A repeatable and short test suited to rapid developmental screening of potential catalyst systems is also presented.

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.

The thermal efficiency of an internal combustion engine at steady state temperatures is typically in the region of 25-35%[1]. In a cold start situation, this reduces to be between 10% and 20% [2]. A significant contributor to the reduced efficiency is poor performance by the engine lubricant. Sub optimal viscosity resulting from cold temperatures leads to poor lubrication and a subsequent increase in friction and fuel consumption. Typically, the engine lubricant takes approximately twenty minutes [3] to reach steady state temperatures. Therefore, if the lubricant can reach its steady state operating temperature sooner, the engine's thermal efficiency will be improved. It is hypothesised that, by decoupling the lubricant from the thermal mass of the surrounding engine architecture, it is possible to reduce the thermal energy loss from the lubricant to the surrounding metal structure in the initial stages of warm-up.

Currently, 80% of European diesel passenger cars are turbocharged and as emission standards become more stringent exhaust gas recirculation (EGR) will be the primary means of suppressing oxides of nitrogen (NOx). The lighter the load the greater will be the combustion tolerance to increased EGR flow rates and hence increased NOx suppression. Automotive diesel engines using wastegated turbochargers cannot recirculate above 50% EGR without some sort of “added” device or system, which is able to displace the inlet fresh air charge. This has been demonstrated by throttling the diesel intake to reduce the fresh air inlet manifold pressure so allowing more EGR flow by virtue of a higher exhaust-side pressure due the effects of the turbocharger. The method reported here investigates a different approach to increasing the EGR rates by replacing a fixed geometry turbocharger (FGT) with a variable geometry turbocharger, (VGT).

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.

With Euro III emissions standards requiring a 50% reduction in current diesel car emissions from January 1st 2000, there is a need to improve fuel consumption and thus reduce exhaust emissions. One of the ways this is being addressed is by the introduction of lower viscosity crankcase oils and by the use of friction modifiers within these oils. The resulting reduced engine friction contributes to improved fuel economy and should also aid engine cold start ability. To investigate the cold start abilities of different crankcase oils, a matrix consisting of six different oil formulations was tested at -20°C, - 25°C and -30°C in a two litre four cylinder diesel engine. The tests were conducted using a dynamometer, with the engine being driven by the dynamometer at nominal cranking speed for 30 seconds and then increased to cold idle speed for a further 30 seconds.

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.

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.

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.