Mr. Xiangyang Wang Profile

Xiangyang Wang

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Proceedings Article | 28 August 2024 Paper

Analysis of driver response based on natural sensors and driving dataset

Xiangyang Wang, Xu Ma, Tian Haodong, Wang Ke

Proceedings Volume 13251, 132516C (2024) https://doi.org/10.1117/12.3039984

KEYWORDS: Roads, Autonomous driving, Linear regression, Autonomous vehicles, Modeling, Unmanned vehicles, Safety, Decision making, Databases

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Driving along the lane occupying the highest proportion of time, and among which the cut-in scenario is the key scenario concerning safety risks. In this paper, cut-in scenario based on the i-Vista natural driving database were extracted. In order to find out the effect of several parameters on drivers' decision of whether, when and how to brake, in-depth data analysis was conducted on road parameters such as vehicle kinematics parameters and environmental parameters by means of logistic regression, linear regression, t-test. The corresponding decision-making mechanism was further discussed. The results showed that the longitudinal speed difference between the two vehicles, the type of road, the indicator of the cut-in vehicle and the light intensity are the main influences that affect driver's decision of whether to brake or not. On the other hand, main factors that significantly affect the driver's braking timing include the longitudinal speed difference between the two vehicles, the speed of the vehicle(time headway), the type of the cut-in vehicle, and the indicator of the cut-in vehicle. The longitudinal speed difference between the two vehicles and the light intensity have a significant impact on the maximum/average deceleration taken by the driver. The relevant conclusions of this article can be used to support the human-like design and the evaluation of autonomous vehicles, and also provide data support for the theoretical research on driver decision-making.

Proceedings Article | 19 July 2024 Paper

Predictive model for hazardous scenarios in autonomous driving based on graph neural networks and variational autoencoders

Haodong Tian, Xiangyang Wang, Xu Ma

Proceedings Volume 13181, 1318176 (2024) https://doi.org/10.1117/12.3031014

KEYWORDS: Unmanned vehicles, Neural networks, Motion models, Autonomous driving, Autonomous vehicles, Feature extraction, Safety

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The evaluation and improvement of autonomous vehicles necessitate the scalable generation of realistic and challenging hazardous scenarios that can be safely resolved. Current research often employs traditional kinematic models to control a single planner, without filtering the generated scenarios for effectiveness. This study introduces a more expressive, datadriven motion prior to control multiple adversarial agents and proposes a novel method for automatically generating challenging scenarios that induce adverse behaviors in a given planner. This paper utilizes an approach that integrates Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Conditional Variational Autoencoders (CVAE). Initially, CNNs extract environmental features for each agent from data sources. Subsequently, GNNs are introduced to understand and leverage the interactions among agents. Finally, CVAEs are used to optimize the latent space of the traffic model to generate trajectories that collide with the given planner. Experimental results demonstrate that this method can generate challenging yet solvable scenarios. It offers a new approach for constructing hazardous scenarios for autonomous vehicles, as well as for behavior planning and trajectory optimization, thereby opening new possibilities for simulation testing of autonomous vehicles.

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