Related papers: A Preference-aware Meta-optimization Framework for…
Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive…
Multi-access edge computing (MEC) is a promising solution for providing the computational resources and low latency required by vehicular services such as autonomous driving. It enables cars to offload computationally intensive tasks to…
Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption.…
Most existing point-of-interest (POI) recommenders aim to capture user preference by employing city-level user historical check-ins, thus facilitating users' exploration of the city. However, the scarcity of city-level user check-ins brings…
The development of autonomous driving has boosted the research on autonomous racing. However, existing local trajectory planning methods have difficulty planning trajectories with optimal velocity profiles at racetracks with sharp corners,…
The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which occurs…
The hybrid electric system has good potential for unmanned tracked vehicles due to its excellent power and economy. Due to unmanned tracked vehicles have no traditional driving devices, and the driving cycle is uncertain, it brings new…
Automated vehicles are gradually entering people's daily life to provide a comfortable driving experience for the users. The generic and user-agnostic automated vehicles have limited ability to accommodate the different driving styles of…
Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions. Current predictive control methods depend on an accurate model of vehicle dynamics and…
With the increasing number of fast-electric vehicle charging stations (fast-EVCSs) and the popularization of information technology, electricity price competition between fast-EVCSs is highly expected, in which the utilization of public…
This paper presents an auto-optimal model predictive control (MPC) framework enhanced with active learning, designed to autonomously track optimal operational conditions in an unknown environment,where the conditions may dynamically adjust…
This study explores the applicability of a graph-based interaction-aware trajectory prediction model, originally developed for the transportation domain, to forecast particle trajectories in three-dimensional discrete element simulations.…
Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these systems assume precise control over autonomous vehicle…
Real-world electric vehicle (EV) energy consumption can deviate by 20-40% from rated values, driven by ambient temperature, traffic congestion, and route characteristics. Existing studies treat these factors in isolation or as static loads,…
Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments. Existing approaches primarily focus on late-fusion techniques, and neglect the importance of…
With the increasing penetration of electric vehicles (EVs) into the automotive market, the electricity peak demand would increase significantly due to home-EV-charging. This paper tackles this problem by defining an 'ideal' EV consumption…
This paper presents a personalized adaptive cruise control (PACC) design that can learn driver behavior and adaptively control the semi-autonomous vehicle (SAV) in the car-following scenario, and investigates its impacts on mixed traffic.…
Accurate vehicle acceleration prediction is critical for intelligent driving control and energy efficiency management, particularly in environments with complex driving behavior dynamics. This paper proposes a general short-term vehicle…
In this paper, we propose a combined energy and comfort optimization (CECO) strategy for the air conditioning (A/C) system of the connected and automated vehicles (CAVs). By leveraging the weather and traffic predictions enabled by the…
The traditional Capacitated Vehicle Routing Problem (CVRP) minimizes the total distance of the routes under the capacity constraints of the vehicles. But more often, the objective involves multiple criteria including not only the total…