Track Ⅸ
Intelligent Assisted Driving Decision-Making and Human-Machine Interaction
(智能辅助驾驶决策与人机交互)
Chair: | Co-chair: |
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Wei Xu | Xiaohua Zhao |
Shandong University of Science and Technology, China | Beijing University Of Technology, China |
Keywords: | Topics: |
· Intelligent Assisted Driving (智能辅助驾驶) · Big Data Analytics (大数据分析) · Decision Control (决策控制) · Human-Machine Interaction (人机交互) | · Optimization Methods for Intelligent Driving Assistance Decision-Making (智能辅助驾驶决策优化方法) · Modeling and Optimization of Intelligent Driving Behavior under Human-Machine Collaboration (人机协同下的智能驾驶行为建模与优化) · Applications of Multimodal Interaction Technology in Intelligent Driving Assistance Systems (多模态交互技术在智能辅助驾驶中的应用) · Research on Driving Decision-Control Algorithms for Complex Scenarios (复杂场景下的驾驶决策控制算法研究) · Data-Driven Techniques for Driving Intention Recognition and Prediction (数据驱动的驾驶意图识别与预测技术) · Explainable Decision-Model Construction for Intelligent Driving Assistance (智能辅助驾驶的可解释性决策模型构建) · Deep Learning-Based Analysis and Optimization of Driving Behavior (基于深度学习的驾驶行为分析与优化) · Real-Time Perception and Decision Fusion in Intelligent Driving Assistance Systems (智能辅助驾驶系统的实时感知与决策融合) · Big Data-Driven Performance Studies of Intelligent Driving Assistance Vehicles (大数据驱动的智能辅助驾驶车辆运行表现) |
Summary:
· Focusing on the field of intelligent assisted driving decision-making and human-machine interaction, this research integrates big data analytics, intelligent decision control, and multimodal interaction technologies. By leveraging big data analytics to extract value from driving scenario data, it employs intelligent algorithms to construct real-time and reliable decision-control models. Through human-machine interaction technologies, it achieves efficient coordination of driving intentions, while exploring data-driven vehicle decision control, driving behavior optimization, human-machine shared driving, and context-adaptive decision-making. The goal is to advance intelligent assisted driving systems in complex traffic environments, enhancing both safety performance and user experience.
· 聚焦智能辅助驾驶决策与人机交互领域，融合大数据分析、智能决策控制与多模态交互技术。通过大数据分析技术挖掘驾驶场景数据价值，结合智能算法构建实时可靠的决策控制模型，利用人机交互技术实现驾驶意图的高效协同，探索数据驱动下的车辆决策控制、驾驶行为优化、人机共驾与场景适应性决策，推动智能辅助驾驶系统在复杂交通环境中的安全提升与用户体验升级。
Submission Deadline: 2025.9.30