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Guotao Xie
Researcher at Hunan University
Publications - 17
Citations - 452
Guotao Xie is an academic researcher from Hunan University. The author has contributed to research in topics: Trajectory & Extensive-form game. The author has an hindex of 7, co-authored 15 publications receiving 248 citations. Previous affiliations of Guotao Xie include Hefei University of Technology & Tsinghua University.
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Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models
TL;DR: Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon than the existing physics- and maneuver-based approaches.
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Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making:
TL;DR: The reward function R of each driver data is established based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed and the efficiency of the proposed method is shown by simulation in a highway environment.
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Situational Assessment for Intelligent Vehicles Based on Stochastic Model and Gaussian Distributions in Typical Traffic Scenarios
TL;DR: The experimental results show that in the dynamic traffic environment, the proposed scenario assessment method can not only accurately predict and assess the situation risks within the prediction range, but also provide accurate scenario risk assessment outside the predictionrange.
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Hardware and software architecture of intelligent vehicles and road verification in typical traffic scenarios
TL;DR: The real road test shows that the designed hardware and software systems for intelligent vehicles have desirable robustness, which can realise accurate and reliable environment perception, decision-making and motion control.
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A Driving Behavior Awareness Model based on a Dynamic Bayesian Network and Distributed Genetic Algorithm
TL;DR: Using naturalistic driving data in Beijing, the comparison between the optimized model and other non-optimized models such as the hidden Markov model (HMM), HMM with a mixture of Gaussian outputs (GM-HMM) indicates that the optimize model could estimate driving behaviors earlier and more accurately.