Ford Motor Company

About: Ford Motor Company is a company organization based out in Dearborn, Michigan, United States. It is known for research contribution in the topics: Internal combustion engine & Clutch. The organization has 36123 authors who have published 51450 publications receiving 855200 citations. The organization is also known as: Ford Motor & Ford Motor Corporation.

Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution

Abstract: Driving style, road geometry, and traffic conditions have a significant impact on vehicles' fuel economy. In general, drivers are not aware of the optimal velocity profile for a given route. Indeed, the global optimal velocity trajectory depends on many factors, and its calculation requires intensive computations. In this paper, we discuss the optimization of the speed trajectory to minimize fuel consumption and communicate it to the driver. With this information the driver can adjust his/her speed profile to reduce the overall fuel consumption. We propose to perform the computation-intensive calculations on a distinct computing platform called the “cloud.” In our approach, the driver sends the information of the intended travel destination to the cloud. In the cloud, the server generates a route, collects the associated traffic and geographical information, and solves the optimization problem by a spatial domain dynamic programming (DP) algorithm that utilizes accurate vehicle and fuel consumption models to determine the optimal speed trajectory along the route. Then, the server sends the speed trajectory to the vehicle where it is communicated to the driver. We tested the approach on a prototype vehicle equipped with a visual interface mounted on the dash of a test vehicle. The test results show 5%-15% improvement in fuel economy depending on the driver and route without a significant effect on the travel time. Although this paper implements the speed advisory system in a conventional vehicle, the solution is generic, and it is applicable to any kind of powertrain structure.

Design for six sigma through robust optimization

Abstract: The current push in industry is focused on ensuring not only that a product performs as desired but also that the product consistently performs as desired. To ensure consistency in product performance, "quality" is measured, improved, and controlled. Most quality initiatives have originated and been implemented in the product manufacturing stages. More recently, however, it has been observed that much of a product's performance and quality is determined by early design decisions, by the design choices made early in the product design cycle. Consequently, quality pushes have made their way into the design cycle, and "design for quality" is the primary objective. How is this objective measured and met? The most recent quality philosophy, also originating in a manufacturing setting, is six sigma. The concepts of six sigma quality can be defined in an engineering design context through relation to the concepts of design reliability and robustness --- probabilistic design approaches. Within this context, design quality is measured with respect to probability of constraint satisfaction and sensitivity of performance objectives, both of which can be related to a design "sigma level". In this paper, we define six sigma in an engineering design context and present an implementation of design for six sigma --- a robust optimization formulation that incorporates approaches from structural reliability and robust design with the concepts and philosophy of six sigma. This formulation is demonstrated using a complex automotive application: vehicle side impact crash simulation. Results presented illustrate the tradeoff between performance and quality when optimizing for six sigma reliability and robustness.

Key-Value Retrieval Networks for Task-Oriented Dialogue

Abstract: Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base. In this work, we seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism. The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. We also release a new dataset of 3,031 dialogues that are grounded through underlying knowledge bases and span three distinct tasks in the in-car personal assistant space: calendar scheduling, weather information retrieval, and point-of-interest navigation. Our architecture is simultaneously trained on data from all domains and significantly outperforms a competitive rule-based system and other existing neural dialogue architectures on the provided domains according to both automatic and human evaluation metrics.