House of Quality

Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever- evolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.

Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connected-vehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.

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Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

The optimal design of a hybrid electric vehicle (HEV) can be formulated as a multiobjective optimization problem that spreads over multiple levels (technology, topology, size, and control). In the last decade, studies have shown that by integrating these optimization levels, fuel benefits are obtained, which go beyond the results achieved with solely optimal control for a given topology. Due to the large number of variables for optimization, their diversity, and the nonlinear and multiobjective nature of the problem, a variety of methodologies have been developed. This paper presents a comprehensive analysis of the various methodologies developed and identifies challenges for future research. Starting from a general description of the problem, with examples found in the literature, we categorize the types of optimization problems and methods used. To offer a complete analysis, we broaden the scope of the search to several sectors of transport, such as naval or ground.

Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles

Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and deep generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.

Deep Generative Design: Integration of Topology Optimization and Generative Models

Deep learning has recently been applied to various research areas of design optimization This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area This work proposes an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance The proposed framework integrates topology optimization and deep generative models (eg, generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options The 2D wheel design problem is applied as a case study for validation of the proposed framework The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods

Car-sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: Autonomous vehicles eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero-emissions. Designing an Autonomous Electric Vehicle (AEV) fleet must account for the relationships among fleet operations, charging station operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four sub-system problems: Fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. A case study for an autonomous fleet operating in Ann Arbor, Michigan, is used to examine AEV sharing system profitability and feasibility for a variety of market scenarios.Copyright © 2015 by ASME

Autonomous Electric Vehicle Sharing System Design

Effective electrification of automotive vehicles requires designing the powertrain’s configuration along with sizing its components for a particular vehicle type. Employing planetary gear systems in hybrid electric vehicle powertrain architectures allows various architecture alternatives to be explored, including single-mode architectures that are based on a fixed configuration and multi-mode architectures that allow switching power flow configuration during vehicle operation. Previous studies have addressed the configuration and sizing problems separately. However, the two problems are coupled and must be optimized together to achieve system optimality. An all-in-one system solution approach to the combined problem is not viable due to the high complexity of the resulting optimization problem. In this paper we propose a partitioning and coordination strategy based on Analytical Target Cascading for simultaneous design of powertrain configuration and sizing for given vehicle applications. The capability of the proposed design framework is demonstrated by designing powertrains with one and two planetary gears for a mid-size passenger vehicle.Copyright © 2015 by ASME

Decomposition-Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design

This paper presents an industrial application of the analytical target cascading methodology to optimal design of commercial vehicle systems. The design problems concern the suspension of a heavy-duty truck and the body structure of a small bus. The results provide valuable insights in the feasibility of system-level design targets and the adequacy of subproblem design spaces during product development.

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Namwoo Kang

Papers

Deep Generative Design: Integration of Topology Optimization and Generative Models

Deep Generative Design: Integration of Topology Optimization and Generative Models

Autonomous Electric Vehicle Sharing System Design

Decomposition-Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design

Optimal design of commercial vehicle systems using analytical target cascading