Conference

Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning

Drivers have unique and rich driving behaviors when operating vehicles in traffic. This paper presents a novel driver behavior learning approach that captures the uniqueness and richness of human driver behavior in realistic driving scenarios. A …

Fuel-Economical Distributed Model Predictive Control for Heavy-Duty Truck Platoon

This paper proposes a fuel-economical distributed model predictive control design (Eco-DMPC) for a homogenous heavy-duty truck platoon. The proposed control strategy integrates a fuel-optimal control strategy for the leader truck with a distributed …

Preliminary Design and Dynamics of a Semi-Expendable Unmanned Ground-Aerial Vehicle

This paper presents an initial concept and design methodology for unmanned vehicles for aerial and ground operation. Modern commercial and military missions require completion of both ground and aerial tasks, with unmanned ground-aerial vehicles …

Personalized Adaptive Cruise Control and Impacts on Mixed Traffic

This paper presents a personalized adaptive cruise control (PACC) design that can learn human driver behavior and adaptively control the semi-autonomous vehicle (SAV) in the car-following scenario, and investigates its impacts on mixed traffic. In …

Inverse Reinforcement Learning Based Driver Behavior Analysis and Fuel Economy Assessment

Human drivers have different driver behaviors when operating vehicles. These driving behaviors, including the driver’s preferred speed and rate of acceleration, impose a major impact on vehicle fuel consumption consequently. In this study, we …

A Predictive Control Design with Speed Previewing Information for Vehicle Fuel Efficiency Improvement

The growing vehicle connectivity and autonomy in the ground transportation system are not only able to improve traffic safety but also fuel efficiency. This paper proposes a receding-horizon optimization-based nonlinear model predictive control …

A Predictive Control Method for Automotive Selective Catalytic Reduction Systems

This paper presents a predictive control method for automotive Selective Catalytic Reduction (SCR) systems to minimize Nitrogen Oxides (NOx) and ammonia (NH3) emission. SCR systems have been indispensable in Diesel-powered vehicles to reduce the …

Model Predictive NOx Emission Control for a Biodiesel Engine Coupled with A Urea-based Selective Catalytic Reduction System.

The applications of biodiesel fuels to Diesel engines have attracted much attention in the past two decades, mainly due to its renewability, biodegradability, and reduced carbon emissions. However, biodiesel-powered engines tend to produce higher NOx …

A Study on Economical Vehicle Platooning Strategy in Urban Driving Scenarios

This paper presents a qualitatively study of driver's behavior impacts on the fuel consumption and travel time of vehicle platooning system under urban driving scenarios. Vehicle platooning is proven advantageous in improving traffic flow, safety, …

Model Based Control of Automotive Selective Catalytic Reduction Systems with Road Grade Preview

This paper introduces a model-based control method for automotive selective catalytic reduction (SCR) systems with preview information of road grade. SCR systems have been widely adopted in Diesel powered ground vehicles to reduce tailpipe NO x …