Lab of Intelligent and Connected Vehicles



Fusion Perception - Complex Environment Perception

1. Vehicle On-board Multi-target Tracking and Motion Prediction Method for Vulnerable Road User

  • Research Background

Vulnerable road users, such as pedestrians, riders or other cyclists, are common and diverse in actual traffic scenarios, yet they are susceptible and need to be protected. The detection is the first step to protect them. It needs to further track the positions of the same target between consecutive frames, form a motion trajectory and achieve multi-target tracking, so as to predict their motion intentions subsequently.

  • Research Achievements

Aiming at the trajectory prediction of vulnerable road users (VRU, including pedestrians and riders) around self-driving vehicles in autonomous driving environment, an in-depth recurrent neural network trajectory prediction method integrating multiple trajectory prediction factors is proposed, taking into account the motion cues such as the positions and shapes of targets between consecutive frames and the deep convolutional feature of local rectangular frame.

In the multi-target trajectory prediction database of vulnerable road users established by Tsinghua-Daimler Joint Research Center for Sustainable Transportation, different variants of recurrent neural network (RNN / LSTM / GRU), target trajectory predictors and network parameter optimization strategies are comprehensively evaluated, and the effectiveness of the proposed VRU multi-target trajectory prediction method is verified.

  • Leading Research Member: XIONG Hui
  • Representative Papers
  1. Xiong H, Flohr F. B., Wang S, Wang, B, Wang, J, and Li, K. Recurrent Neural Network Architectures for Vulnerable Road User Trajectory Prediction[C].2019 IEEE Intelligent Vehicles Symposium (IV),IEEE, 2019: 171-178.
  2. Huang B, Xiong H, Wang J, Xu Q, Li X, and Li K. Detection-level fusion for multi-object perception in dense traffic environment[C].2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI),IEEE, 2017: 411-416.

2. Multi-target Tracking Based on Roadside Camera 

  • Research Background

Accurate and robust multi-target tracking method is the premise of cognition and decision-making for intelligent and connected vehicles, and plays an important role in the automatic driving perception technology. And the roadside camera has the prominent advantages of low cost, rich sensing information and wide field of vision, etc. Therefore, the multi-target tracking method based on roadside camera is of great significance for the realization of vehicle-road coordinated perception and high-level automatic driving.

3. Estimation Method of Tire-road Adhesion Coefficient Based on Current On-board Sensors

  • Research Background

Tire-road adhesion coefficient is a considerable parameter for describing the current tire-road condition, and also an important prior knowledge of automatic driving system decision-making and control algorithm. It is of great significance to the safety and comfort of automatic driving system. Besides, for the connected traffic system, the estimation results of the adhesion coefficient can be shared, providing early warning for the driver and automatic driving system, thereby greatly enhancing the safety of the whole traffic system.

  • Research Achievements

Based on 2D-LuGre tire model and Unscented Kalman Filtering, the real-time estimation of tire-road adhesion coefficient under three driving conditions of braking, acceleration and steering is realized. The observable condition of the established tire system is proved, and the theoretical boundary of dynamic-based estimation method of tire-road adhesion coefficient is put forward. Through the co-simulation of CarSim-MATLAB and Simulink, the effectiveness of the proposed method is preliminarily verified.

  • Leading Research Member: LIN Xuewu
Created: Mar 05, 2021 | 08:56