Search Results

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.

Latches are generally used to lock/unlock a component against each other. In the automotive industry, latches are widely used in doors and seats. Seat latches have to secure the seat safely to the body in the event of a crash and at the same time they have to be locked/unlocked with easy efforts. Seat latches are mostly supplier designed parts. Supplier latch effort calculations involve only latch components. Actual latch effort calculations should be done with seat structures, foams, trims and body environments. Hence OEMs are responsible to provide easily lockable/unlockable seats to their customers. Customers nowadays, are raising complaints regarding latching issues to respective automotive industry which in turn costs more due to after sales services/warranty claims. Therefore, automotive industries must spend a significant amount of time and capital on physical test and method development for calculating the latch efforts.

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 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.

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.

Automotive Seat design is driven by various design factors considering occupant safety, occupant comfort, road visibility etc. Seats are generally a combination of structural frames, foams, suspension wires, adjustment mechanisms etc. to support safety, comfort & road visibility. Most of the regulatory tests on seats are related to safety which demands structural integrity of seat frames, welds and bolts etc. And few tests demand occupant comfort and seat functionality involving foams, suspension wires & adjustment mechanisms. There are few tests which needs design consideration on all seat components discussed above. FMVSS 202a Back Set Retention is one among them. It is a static test with multiple loads in sequence of loading and unloading steps on the seat back and head restraint. Stiffness of the foams, lumbar mats, seat adjustment mechanisms, seat frames contribute a lot to meet performance targets set by the regulation.

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.

The rapid heating of an under-body catalyst after cold start by combustion of rich engine products with added secondary air is described. The results of initial durability studies including spark plug fouling, oil dilution and thermal shock are presented, together with emissions performance and a mileage accumulation study. Also discussed are failure mode assessment and the system tolerance to anticipated open-loop errors and real-world driving scenarios.

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.

Position of the H-point plays a vital role during designing the seating system. The seating system provides support and comfort to the occupants while they are operating the vehicle. The traditional way to design a seat system is to use rules of thumb and experience, which often results in several costly design iterations. The purpose of this paper is to demonstrate the capability of CAE analytical tool to find the H-point at the early phase of the seating system design without compromising the comfort level of the occupant. The recently launched Lincoln Continental front seating system was used to validate this purpose. The Continental seating system has unique design features which provide special challenges in designing and simulating the seat. With the help of CAE analytical tool, the traditional process is streamlined and a seat design could be achieved in a shorter period with greater accuracy.

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.

Accurate road friction coefficient is crucial for the proper functioning of active chassis control systems. However, road friction coefficient is difficult to be measured directly. Using the available onboard sensors, a model-based Extended Kalman filter (EKF) algorithm is proposed in this paper to estimate road friction coefficient. In the development of estimation algorithm, vehicle motion states such as sideslip angle, yaw rate and vehicle speed are first estimated. Then, road friction coefficient estimator is designed using nonlinear vehicle model together with the pre-estimated vehicle motion states. The proposed estimation algorithm is validated by both simulations and tests on a scaled model vehicle.

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.

Air filter elements have been around since the dawn of automotive development. The function of an air induction system and the filter element in particular is to remove particulates such as dust, soot, and relatively minor contaminants from the air flow. This protects the engine, turbocharger, and other components from wear. However, sometimes severe duty cycles may cause large amounts of dust, mud, and water to enter the air induction system (AIS). This can cause filter degradation and even rupture or deformation, leading to highly increased engine and turbocharger wear. One example of this extreme loading is the tar sands region of Alberta, Canada, where trucks can accumulate over 1000 pounds of mud on a vehicle during normal usage over a few weeks’ time. Significant amounts of this mud also get ingested into the AIS. This study attempts to analyze different aspects of filter design to increase robustness to severe usage, particularly mud.

Manufacturing processes often produce a large amount of heat, which needs to be pumped out of the factory to maintain thermal stability and comfort. Thermal comfort is essential to maintain a suitable working environment in a factory. It has a strong impact on the health and productivity of workers. In addition, it is mandatory to keep the working environment within specified thermal and relative humidity ranges. Periodic assessments of these thermal parameters is routine in most factories. Inclusion of additional manufacturing equipment or processes can lead to a significant change in the working environment and consequent comfort, this needs to be addressed quickly. Rather than wait to measure these effects it is preferable to develop a reliable simulation method for the proactive study and improvement of thermal comfort levels. A reliable simulation approach is developed in this study for the prediction of thermal comfort in an automotive manufacturing plant.

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.

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.