面向无人驾驶汽车主要研究方向包括：

（1）图像与激光融合场景重建

（2）车辆多模态交互轨迹预测

（3）基于车路协同的场景目标感知与风险评估

主持和参研项目：

1.国家重点研发计划“智能电动汽车电子电气架构研发” 2017-2020
2.自然科学基金面上项目 “基于局部群体联合动力学的目标车辆侧向状态精准估计方法研究”2021-2024
3.四川省科技计划项目 “面向矿车特殊工况的无人驾驶矿卡感知与定位技术研究” 2020-2021
4.企业横向“虚拟平台激光雷达点云仿真模型开发” 2023-2024

发表论文

[1]Liu X, Wang Y , Zhou Z , Wei C, Yin C. Trajectory Prediction of Preceding Target Vehicles Based on Lane Crossing and Final Points Generation Model Considering Driving Styles[J]. IEEE Transactions on Vehicular Technology, 2021, 70(9):8720-8730.

[2]Liu X, Wang Y , Jiang K , Zhou Z, Nam K, Yin C. Interactive Trajectory Prediction Using a Driving Risk Map-Integrated Deep Learning Method for Surrounding Vehicles on Highways[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10):19076-19087.

[3]Liu X, Jin G , Wang Y , Yin C. A Deep Learning-based Approach to Line Crossing Prediction for Lane Change Maneuver of Adjacent Target Vehicles[C]. 2021 IEEE International Conference on Mechatronics (ICM), Japan, 2021:1-6. 

[4]Liu X, Wang Y, Duan B, Zhou G, Yin C. Trajectory Prediction for Human-driven Vehicles Based on a Combined Method of Deep Neural Network and Driving Risk Map[C]. 2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI), Nan Jing, China, 2021：1-6.

[5]Zhang Z, Wang Y, Liu X. TAPNet: Enhancing Trajectory Prediction with Auxiliary Past Learning Task[C]. 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya,Japan, 2021:421-426. 

[6]Wu S, Wang Y, Liu X, Hu J. ATDS: Adaptive Temporal and Dual Spatial Encoders for Motion Forecasting[C]. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China , 2022:434-439.

[7]Chen Z, Wang Y, Liu X, Wang X. FS-GRU: Continuous Perception and Prediction with inter Frame Feature Sharing[C]. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China , 2022:517-522.

承担新能源汽车专业《新能源汽车电子控制技术》、《新能源汽车电力电子基础》的教学工作