SHITIANYU (@SHITIANYU-hue)
github

SHITIANYU-hue

Twitter logo Share LinkedIn logo Share Facebook logo Share

Top repositories

1

Efficient-motion-planning

To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles
MATLAB
61
star
2

Data-driven-control

A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle sub-systems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feed-forward compensator is designed via a data-driven method to model and optimize the controller’s performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.
MATLAB
44
star
3

COMP-767-project

This is the source code of COMP 767 group project of Tianyu Shi & Jiawei Wang.
Python
6
star
4

dgn_ring_torch

Jupyter Notebook
3
star
5

AI-follow

梳理每周最新多模态，LLMs,embodied AI相关论文
3
star
6

agebias

process for age bias dataset
Python
2
star
7

Traffic-Speed-Control-System

Python
1
star
8

kaggle-bio

https://www.kaggle.com/competitions/open-problems-multimodal/
Jupyter Notebook
1
star
9

sumosim

A sumo based simulator that can support both micro and macro level control
Jupyter Notebook
1
star