A5054083407- Autonomous Vehicle Technology and Safety
- Traffic and Road Safety
- Human-Automation Interaction and Safety
- Vehicle Dynamics and Control Systems
- Infrastructure Maintenance and Monitoring
- Traffic control and management
- Vehicle emissions and performance
- Advanced Neural Network Applications
- Transportation and Mobility Innovations
- Neural Networks and Applications
- Vehicle License Plate Recognition
- Anomaly Detection Techniques and Applications
- Robotics and Automated Systems
- Time Series Analysis and Forecasting
- Simulation and Modeling Applications
- Advanced Measurement and Detection Methods
- Industrial Technology and Control Systems
- Real-time simulation and control systems
- Network Security and Intrusion Detection
- Safety Systems Engineering in Autonomy
- Transport Systems and Technology
- Cognitive Computing and Networks
- Urban Heat Island Mitigation
- Remote Sensing and Land Use
- Structural Health Monitoring Techniques
Beihang University
2022-2025
Beijing Institute of Technology
2025
Fujian Normal University
2024
Xi'an Polytechnic University
2023
Driving safety is the most basic demand for autonomous vehicles but also difficult task at same time. Intelligent tires equipped with in-tire sensors have been proposed to recognize tire-road interaction conditions, such as road surface tire dynamics states, and friction which can help realize safer driving of vehicles. In this study, we first review related research intelligent about sensing system contact state estimation methods discuss their desirability limitations. On basis, propose...
How to drive safely in complex real-world traffic settings has long been a question and challenge for autonomous vehicles (AVs). Decision-making systems (DecSs) are the core of AVs, their safety rationality crucial. Several decision-making techniques algorithms have applied AVs; however, they still subject limitations deficiencies, making it impossible fully guarantee safety. Thus, is necessary conduct assessment state performance DecSs AVs reduce driving risks implement immediate measures....
Autonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels driving assist systems to reduce the human driver’s burden. However, because conservative design its programming framework, there is still a large gap between performance current autonomous and experienced veteran drivers. This can cause drivers distrust decisions or behaviors made by vehicles, thus affecting effectiveness drivers’ use...
Analysis of the autonomous vehicles accidents is one best ways to provide support for improving auto driving systems. This paper uses publicly available dataset from California DMV deeply study problem current The original data processed according its features. Some new definition has been made clearly analyze precise reason vehicle under mode. result indicates that rear-ended collision main type accident. Based on some further analysis, possible causes accident related inadequate system are...
The perception of road conditions is great significance for optimising the driving safety and comfort vehicles. intelligent tyre system considered a reliable solution recognizing using data collected by sensors installed in tyres corresponding algorithms. However, existing systems usually adopt point-type measurements, limiting ability to perceive conditions. In this study, based on finite element analysis, an piezoelectric cables designed condition perception. Through those locations...
The Autonomous Vehicle (AV) has become a hot research spot recently. Following the trend of auto driving (AD) development, making AV safer is evolving as new direction research. decision system in AD can be regarded brain vehicle, which ensures safety and comfort behaviors. However, technologies systems, including algorithms hardware, remain incomplete could affect AV. Hence, it necessary to focus on systems before AVs are widespread application. In this paper, existing techniques for...
The motion intentions and future trajectories of traffic participants have great influences on the decision-making path planning processes autonomous vehicles. Lane change behaviors needs to be studied with accurate mathematical representation realize long-term reliable intention trajectory prediction. Traditional studies applied specific probability model perform prediction, but this is limited by strict assumptions constraints. With development deep learning methods, better prediction...
Route planning for autonomous driving is a global road method based on given starting point and target combined with current traffic flow information. The optimal route can reduce jams improve the safety economy of vehicles. optimization only considers single objective or chain objectives, which cannot meet requirements drivers. In this paper, we devise general framework multi-objective optimization. Different from not information, travel time, distance, cost personal preference, but focuses...
<div class="section abstract"><div class="htmlview paragraph">Recognizing road conditions using onboard sensors is significant for the performance of intelligent vehicles, and profile a widely accepted representation both in temporal frequency domains, greatly influencing driving quality. In this paper, recurrent neural network embedded with attention mechanisms proposed to reconstruct sequence. Firstly, vehicle sensor signals are obtained simulated environment by modeling road,...
This paper proposed an adaptive prediction model for high-dimensional time series data based on averaging method and principal component analysis. Specifically, this considers the case where response variable is a scalar predictor series. Firstly, extracted information by Secondly, Bayesian used to perform forecast task projection matrix. The can effectively deal with unsupervised nature of PCA avoid problem selecting number PCA. It demonstrated that competitive compared lasso regression...
Abstract Safety represents the fundamental bottleneck in development of autonomous driving.The majority current research focuses on driving safety regular scenarios. However, certain actions by other vehicles may pose significant risks to ego vehicle, rustling emergency In this paper, we propose an escape strategy for vehicles, encompassing risk assessment, trajectory generation, and Model Predictive Control (MPC) based control schemes. assessment module, introduce Fusion Time-toCollision...
As the demand for private vehicles rises, there has been a gradual increase in number of motor on roads, leading to growing concern about addressing traffic safety. Currently, China’s approach assessing driver capabilities remains rooted traditional, non-intelligent, and standardized evaluation methods based examination subjects. The traditional model often falls short providing constructive feedback driver’s real-world vehicle handling abilities, as many subjects can be practiced advance...
As autonomous vehicles gain consumer favor, ensuring their operational safety has become a crucial aspect of usage. For those vehicles, accurate scene perception and recognition the environment are paramount objectives for safe decision-making control strategies. To ensure proper comprehension driving scenarios, development reasonable definition methodologies along with highly efficient rapid technologies focal point interest among researchers. This paper, based on an analysis shortcomings...
Road conditions can significantly influence the safety of autonomous vehicles (AV). Existing sensors AV are usually less effective in recognizing road during night and inclement weather. While intelligent tire systems, identify different accurately hardly be affected by weather or illumination conditions. However, data collected under working differ from each other with significant shifts hard to collect due tremendous field experiments. To this end, we propose a domain-adaptive model that...