Shared Mobility-on-Demand using automated vehicles can reduce energy consumption and cost for future mobility. However, its full potential in energy saving has not been fully explored. An algorithm to minimize fleet fuel consumption while satisfying customers' travel time constraints is developed in this article. Numerical simulations with realistic travel demand and route choice are performed, showing that if fuel consumption is not considered, the Mobility-on-demand (MOD) service can increase fleet fuel consumption due to increased empty vehicle mileage. With fuel consumption as part of the cost function, we can reduce total fuel consumption by 7% while maintaining a high level of mobility service.

Automated vehicles can change the society by improved safety, mobility, and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to interact with many human-driven vehicles. In the past, the control/design of the highly automated (robotic) vehicles mainly considers safety and efficiency but failed to address the “driving culture” of surrounding human-driven vehicles. Thus, the robotic vehicles may demonstrate behaviors very different from other vehicles. We study this “driving etiquette” problem in this paper. As the first step, we report the key behavior parameters of human driven vehicles derived from a large naturalistic driving database. The results can be used to guide future algorithm design of highly automated vehicles or to develop realistic human-driven vehicle behavior model in simulations.

A nonparametric fuel consumption model is developed and used for eco-routing algorithm development in this paper. Six months of driving information from the city of Ann Arbor is collected from 2,000 vehicles. The road grade information from more than 1,100 km of road network is modeled and the software Autonomie is used to calculate fuel consumption for all trips on the road network. Four different routing strategies including shortest distance, shortest time, eco-routing, and travel-time-constrained eco-routing are compared. The results show that eco-routing can reduce fuel consumption, but may increase travel time. A travel-time-constrained eco-routing algorithm is developed to keep most the fuel saving benefit while incurring very little increase in travel time.

An algorithm to cluster mobility-on-demand trips considering road network structure is developed in this paper. The benefits of our network partition algorithm are demonstrated in numerical simulations, showing that we can use fewer vehicles and can serve more customers with slightly longer wait time by including predicted future travel demand in trip assignment, compared with the benchmark reactive control policy.

The safety of automated vehicles (AVs) must be assured before their release and deployment. The current approach to evaluation relies primarily on 1) testing AVs on public roads or 2) track testing with scenarios defined in a test matrix. These two methods have completely opposing drawbacks: the former, while offering realistic scenarios, takes too much time to execute and the latter, though it can be completed in a short amount of time, has no clear correlation to safety benefits in the real world. To avoid the aforementioned problems, we propose accelerated evaluation, focusing on the car-following scenario. The stochastic human-controlled vehicle (HV) motions are modeled based on 1.3 million miles of naturalistic driving data collected by the University of Michigan Safety Pilot Model Deployment Program. The statistics of the HV behaviors are then modified to generate more intense interactions between HVs and AVs to accelerate the evaluation procedure. The importance sampling theory was used to ensure that the safety benefits of AVs are accurately assessed under accelerated tests. Crash, injury and conflict rates for a simulated AV are simulated to demonstrate the proposed approach. Results show that test duration is reduced by a factor of 300 to 100 000 compared with the non-accelerated (naturalistic) evaluation. In other words, the proposed techniques have great potential for accelerating the AV evaluation process.

Dedicated short range communication (DSRC) was designed to provide reliable wireless communication for intelligent transportation system applications. Sharing information among cars and between cars and the infrastructure, pedestrians, or “the cloud” has great potential to improve safety, mobility, and fuel economy. DSRC is being considered by the U.S. Department of Transportation to be required for ground vehicles. In the past, their performance has been assessed thoroughly in the laboratories and limited field testing, but not on a large fleet. In this paper, we present the analysis of DSRC performance using data from the world's largest connected vehicle test program-Safety Pilot Model Deployment lead by the University of Michigan. We first investigate their maximum and effective range, and then study the effect of environmental factors, such as trees/foliage, weather, buildings, vehicle travel direction, and road elevation. The results can be used to guide future DSRC equipment placement and installation, and can be used to develop DSRC communication models for numerical simulations.

An algorithm is developed to optimize vehicle speed trajectory over multiple signalized intersections with known traffic signal information to minimize fuel consumption and travel time, and to meet ride comfort requirements using sequential convex optimization method. A comparison between the proposed method and dynamic programming is carried out to verify its optimality. In addition, vehicle motion during turning is studied because of its significant effect on fuel consumption and travel time.

Broadband N2 vibrational coherent anti-Stokes Raman scattering (VCARS) thermometry was performed along the nozzle centerline of counter-flow diffusion flames. Fuel mixtures consisted of varying percentages of H2 in N2 and the oxidizer was air. We investigated the influence of varying the percentage of H2 in the fuel stream on the temperature profiles along the nozzle centerline. Measurements were performed, with varying global strain rates, both near and far from flame extinction. One-dimensional simulations, performed using Combustion Simulation Lab (COSILAB) software, show that the temperature profiles are relatively insensitive to the nozzle exit centerline velocity; implying that the 1-D simulations provide an excellent approximation of the real temperature profiles along the nozzle centerline. Various chemical mechanisms were employed within the COSILAB framework for comparison against VCARS temperature measurements. Simulations were also performed to evaluate the influence of thermal diffusion and nitrogen chemistry on nozzle centerline temperature profiles.

Powertrain diagnosis has been demanded with growth & complexity of powertrain electronic control system and enforcement of law & regulation in the last decades. In regulation OBD II, requirement of misfire monitoring has been demanded much more strictly. A variety of diagnosis methods for misfire have been proposed and developed, however most of them either depend greatly on special or expensive sensors or suffer from the disturbance of vibration due to non-misfire reasons. One combination of Frequency Domain Analysis and Fuzzy Logic to perform the misfire diagnosis is proposed. It takes full advantage of property of frequency domain analysis and fuzzy logic, providing accurate and robust detection results, without adding additional hardware diagnosis instruments. Fast Fourier Transform is used to perform frequency domain analysis, which can abstract the specific frequency characteristics of misfire signals during overall engine crankshaft speed fluctuations directly and quickly, and fuzzy logic provides good trading off between significance and precision, which can reduce possible false misfire alarm caused by interruption of noise and disturbance of uneven road. The experiment on one four-cylinder engine proves that the method is effective.