G. Li
13 records found
1
Lateral conflict resolution data derived from Argoverse-2
Analysing safety and efficiency impacts of autonomous vehicles at intersections
With the increased deployment of autonomous vehicles (AVs) in mixed traffic flow, ensuring safe and efficient interactions between AVs and human road users is important. In urban environments, intersections have various conflicts that can greatly affect driving safety and traffic
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How predictable are macroscopic traffic states
A perspective of uncertainty quantification
Traffic condition forecasting is fundamental for Intelligent Transportation Systems. Besides accuracy, many services require an estimate of uncertainty for each prediction. Uncertainty quantification must consider the inherent randomness in traffic dynamics, the so-called aleator
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Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In pr
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Resolving predicted conflicts is vital for safe and efficient autonomous vehicles (AV). In practice, vehicular motion prediction faces inherent uncertainty due to heterogeneous driving behaviours and environments. This spatial uncertainty increases non-linearly with prediction ti
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Beyond behavioural change
Investigating alternative explanations for shorter time headways when human drivers follow automated vehicles
Integrating Automated Vehicles (AVs) into existing traffic systems holds the promise of enhanced road safety, reduced congestion, and more sustainable travel. Effective integration of AVs requires understanding the interactions between AVs and Human-driving Vehicles (HVs), especi
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Continual driver behaviour learning for connected vehicles and intelligent transportation systems
Framework, survey and challenges
Modelling, predicting and analysing driver behaviours are essential to advanced driver assistance systems (ADAS) and the comprehensive understanding of complex driving scenarios. Recently, with the development of deep learning (DL), numerous driver behaviour learning (DBL) method
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Traffic dynamics on freeways are stochastic in nature because of errors in perception and operation of drivers as well as the heterogeneity between and within drivers. This stochasticity is often represented in car-following models by a stochastic term, which is assumed to follow
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Large Car-following Data Based on Lyft level-5 Open Dataset
Following Autonomous Vehicles vs. Human-driven Vehicles
Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Res
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Observing, modelling, predicting, and understanding the dynamics of traffic systems on different levels is one of the most critical topics in the transport and planning domain. At the macroscopic scale, traffic congestion is the central problem that impacts all aspects of society
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UQnet
Quantifying Uncertainty in Trajectory Prediction by a Non-Parametric and Generalizable Approach
Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modeling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. We nee
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Estimate the limit of predictability in short-term traffic forecasting
An entropy-based approach
Accurate short-term traffic forecasting is the cornerstone for Intelligent Transportation Systems. In the past several decades, many models have been proposed to continuously improve the predictive accuracy. A key but unsolved question is whether there is a theoretical bound to t
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Multistep traffic forecasting by dynamic graph convolution
Interpretations of real-time spatial correlations
Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction method
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Dynamic Graph Filters Networks
A Gray-box Model for Multistep Traffic Forecasting
Short-term traffic forecasting is one of the key functions in Intelligent Transportation System (ITS). Recently, deep learning is drawing more attention in this field. However, how to develop a deep learning based traffic forecasting model that can dynamically extract explainable
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