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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.

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.

The cycle-by-cycle variation of heat transferred per cycle (q) to the combustion chamber surfaces of spark ignition engines has been investigated for quasi-steady and transient conditions produced by throttle movements. The heat transfer calculation is by integration of the instantaneous value over the cycle, using the Woschni correlation for the heat transfer coefficient. By examination of the results obtained, a relatively simple correlation has been identified: This holds both for quasi-steady and transient conditions and is on a per cylinder basis. The analysis has been extended to define a heat flux distribution over the surface of the chamber. This is given by: where F(x/L) is a polynomial function, q″ is the heat transfer per cycle per unit area to head and piston crown surfaces and gives the distribution along the liner

Previously reported studies of heat transfer between the intake port surface, gas flows in the port, and fuel deposited in surface films have been extended to examine details of the heat flux variations which occur within the engine cycle. The dynamic response characteristics of the surface-mounted heat flux sensors have been determined, and measured heat flux data corrected accordingly to account for these characteristics. Details of the model and data processing technique used are described. Corrected intra-cycle variations of heat transfer to fuel deposited have been derived for engine operating conditions at 1000 RPM covering a range of manifold pressures, fuel supply rates, port surface temperatures, and fuel injection timings. Both pump-grade gasoline and isooctane fuel have been used. The effects of operating conditions on the magnitude and features of the heat flux variations are described.

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.

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.

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.

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.

Seat assessment is an important necessity for the growing auto industry. The design of seats is driven by customer’s demand of comfort and aesthetics of the vehicle interiors. Some of the few seat assessments are H-point prediction with H-point Machine (HPM); backset prediction with Head Restraint Measuring Device (HRMD); seat hardness and softness. Traditional seat development was through developing series of prototypes to meet requirements which involved higher costs and more time. The seat requirement of H-Point measurement is of focus in this paper. Though there are other commercial available software/methods to perform the H-point measurement simulations, the aim here was to assess the capabilities of an alternate Computer Aided Engineering (CAE) methodology using CAE tools - PRIMER and LS-Dyna. The pre-processing tools - Hypermesh and ANSA have been used for modeling and Hyperview tool used for reviewing the simulations.