A5021950355- Vehicle Dynamics and Control Systems
- Autonomous Vehicle Technology and Safety
- Traffic control and management
- Control Systems and Identification
- Advanced Control Systems Optimization
- Adsorption and biosorption for pollutant removal
- Hydraulic and Pneumatic Systems
- Adaptive Control of Nonlinear Systems
- Stability and Control of Uncertain Systems
- Cosmology and Gravitation Theories
- Fault Detection and Control Systems
- Robotic Path Planning Algorithms
- Real-time simulation and control systems
- Environmental remediation with nanomaterials
- Advanced oxidation water treatment
- Automotive and Human Injury Biomechanics
- Vibration Control and Rheological Fluids
- Infrastructure Maintenance and Monitoring
- Advanced Chemical Sensor Technologies
- Phosphorus and nutrient management
- Structural Health Monitoring Techniques
- Arsenic contamination and mitigation
- Probabilistic and Robust Engineering Design
- Soil Mechanics and Vehicle Dynamics
- Robotics and Sensor-Based Localization
Ford Motor Company (United States)
2015-2025
Yantai University
2017-2024
Shandong University of Science and Technology
2024
Nanning Normal University
2018-2024
Chinese National Human Genome Center
2024
Hohai University
2022-2024
Guangxi University
2023
Research Center for Eco-Environmental Sciences
2012-2022
Tohoku University
2022
PLA Rocket Force University of Engineering
2021
The problem of reorienting a rigid spacecraft as fast possible within the physical limits actuators and sensors is investigated. In particular, nonlinear feedback control logic which accommodates actuator sensor saturation introduced. nearminimum-time eigenaxis reorientation Xray Timing Explorer under slew rate torque constraints used an example to demonstrate effectiveness simplicity proposed logic.
Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which becoming increasingly mature and accurate, but it tends be fragile under challenging environments. Comparing classical geometry-based methods, deep learning-based methods can automatically learn effective robust representations, such as depth, optical flow, feature, ego-motion, etc., from data without explicit computation. Nevertheless, there still lacks thorough review of recent advances VO (Deep...
In the context of automated driving, navigating through challenging urban environments with dynamic objects, large-scale scenes, and varying lighting/weather conditions, achieving accurate localization is paramount for highly-automated (HAVs) or autonomous vehicles (AVs). An imprecise can greatly impact subsequent decision-making to manage an HAV AV's motion (planning control tasks). recent years, visual simultaneous mapping (VSLAM) has shown substantial progress equipping it lead handling...
Cu2O/Ag composite nanospheres (CNSs) with tunable Ag coverage and optical properties have been prepared based on a one-pot room temperature method by adding AgNO3 solution to fresh Cu2O nanosphere-produced mother in various ratios. Ag+ ions can be reduced the primary acidic solution, obtained nanoparticles deposited surfaces of nanospheres. The photocatalytic activity CNSs has evaluated for photodegradation methyl orange (MO) dye under visible-light irradiation, which demonstrates that...
This paper addresses the trajectory planning problem for autonomous vehicles in traffic. We build a stochastic Markov decision process (MDP) model to represent behaviors of vehicles. MDP takes into account road geometry and is able reproduce more diverse driving styles. introduce new concept, namely, "dynamic cell," dynamically modify state traffic according different vehicle velocities, driver intents (signals), sizes surrounding (i.e., truck, sedan, so on). then use Bézier curves plan...
The precise identification of surface imperfections in steel strips is crucial for ensuring product quality. To address the challenges posed by substantial model size and computational complexity current algorithms detecting defects strips, this paper introduces SS-YOLO (YOLOv7 Steel Strip), an enhanced lightweight YOLOv7 model. This method replaces CBS module backbone network with a MobileNetv3 network, reducing accelerating inference time. D-SimSPPF module, which integrates depth separable...
A multiobjective optimal control strategy is pursued for finding feedback laws used in controlled suspensions automotive vehicles. The balanced vehicle ride and handling performances are the main concern of paper. performance (car body performance) characterized by an H/sub 2/ system norm, (wheel /spl infin// method optimizes mixed 2//H/sub performances. norms optimization scaled corresponding open-loop such that relative importance individual variables can be reflected index vector...
We present a dynamic stability and agility study of pendulum-turn vehicle maneuver. Instead optimizing the controlled inputs to mimic expert human driver performance, we focus on understanding performance motion using professional racing car testing data. propose use rear side slip angle, rather than mass center as one state variable obtain precise stable region. A hybrid physical/dynamic tire/road friction model is used capture force characteristics. also introduce lateral jerk acceleration...
For the first time vapour sensors were made by assembling multi-wall carbon nanotube (CNT) decorated poly(methyl methacrylate) microbeads (PMMAµB) spray layer (sLbL). This combination of materials and technique resulted in an original hierarchical architecture with a segregated network CNT bridging PMMAµB. The chemo-resistive behaviour these conductive polymer nanocomposite (CPC) was studied terms sensitivity selectivity towards standard volatile organic compounds (VOC), as well...
This paper investigates a comfort-based route planner that considers both travel time and ride comfort. We first present framework of simultaneous road profile estimation anomaly detection with commonly available vehicle sensors. A jump-diffusion process-based state estimator is developed used along multi-input observer for estimation. The evaluated in an experimental test promising performance demonstrated. Second, three objective comfort metrics are based on factors such as time,...
This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate segment risk, accident database from the highway safety information system is mined with hybrid neural network model predict RRI. Real-time factors such time of day, day week, weather included correction static RRI prediction. With real-time expected travel time, planning...
This paper studies the decision making problem of autonomous vehicles in traffic. We model interaction between an vehicle and environment as a stochastic Markov process (MDP) consider driving style experienced driver target to be learned. The road geometry is taken into consideration MDP order incorporate more diverse styles. By designing reward function MDP, desired, behavior obtained using reinforcement learning. Simulated results demonstrate desired behaviors vehicle.