Hacktoberfest 2026: những issue maintainer đã đánh dấu cho tháng Mười, đang mở và phù hợp người mới. Xem issue Hacktoberfest

Deep Learning-based Trajectory Prediction

Đang mở
  • Có gì không ổn?
#51 3 bình luận 0 reaction 1 người được giao Xem trên GitHub

Maintainer thường phản hồi trong vòng 9 ngày

@Khushi0512 đang làm issue này rồi.

Từ ngày 7/2/2026.

Đánh giá

Issue này chưa được đánh giá.

Mô tả

enhancement

Algorithm/Conceptual basis

LSTM-GRU Encoder-Decoder Network for Multi-Vehicle Trajectory Prediction

Description & Application

This deep learning model forecasts the future behaviors of surrounding vehicles by utilizing a recurrent neural network, more specifically LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) networks. Taking the input as a historical state trajectory data (position, velocity, acceleration), the model is then required to predict future positions over a prediction horizon (typically 3-5 seconds).
The encoder-decoder architecture deals with temporal sequences: the encoder that extracts the features out of previous trajectories and a decoder which produces predictions on future ones. Further, an attention mechanism can be used to capture the vehicle's interactions and determine which other vehicles affect the target vehicle's trajectory more. This is essential for self-driving cars to predict and respond to the action of traffic participants in their vicinity.

Plan for Implementation

  1. Data Generation/Collection:

  • Simulate or use real highway trajectory datasets (NGSIM, HighD)

  • Generate synthetic multi-vehicle scenarios with various maneuvers

  1. Data Preprocessing:

  • Extract features: position (x, y), velocity (vx, vy), acceleration (ax, ay), heading

  • Normalize/standardize trajectory data

  • Create sliding window sequences (observation window: 3s, prediction horizon: 5s)

  1. Model Architecture:

  • LSTM Encoder: Process historical trajectory sequences

  • GRU Decoder: Generate future trajectory predictions

  • Attention Layer: Model vehicle interactions (optional)

  • Fully Connected Output: Map to predicted coordinates

  1. Training:

  • Loss function: Mean Squared Error (MSE) or Mean Absolute Error (MAE)

  • Optimizer: Adam with learning rate scheduling

  • Validation: K-fold cross-validation

  1. Comparative Analysis: Implement and compare vanilla RNN, LSTM, GRU, Bi-LSTM models
  2. Visualization: Plot predicted vs. actual trajectories, attention weights, error distributions

References in Consideration

  1. Kaouther Messaoud, Itheri Yahiaoui, Anne Verroust-Blondet, Fawzi Nashashibi, "Attention Based
    Vehicle Trajectory Prediction. IEEE Transactions on Intelligent Vehicles, Institute of Electrical and
    Electronics Engineers", In press, 10.1109/TIV.2020.2991952. hal-02543967
    https://www.researchgate.net/publication/341055264_Attention_Based_Vehicle_Trajectory_Prediction
  2. Jiaqin Wang, Kai Liu, Hantao Li, "LSTM-based Graph attention network for vehicle trajectory prediction", Computer Networks, volume 248, June 2024, 110477
    https://www.sciencedirect.com/science/article/abs/pii/S1389128624003098
  3. Zhao, Tianyang & Xu, Yifei & Monfort, Mathew & Choi, Wongun & Baker, Chris & Zhao, Yibiao & Wang, Yizhou & Wu, Yingnian. (2019). Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. 12118-12126. 10.1109/CVPR.2019.01240.
    https://www.researchgate.net/publication/338509582_Multi-Agent_Tensor_Fusion_for_Contextual_Trajectory_Prediction

Expected Outcomes

  • Trained models achieving <1.5m prediction error at 3s horizon, <3m at 5s horizon

  • Comparison showing GRU achieving similar/better accuracy than LSTM with fewer parameters

  • Visualization of predicted trajectories overlaid on ground truth

  • Attention heatmaps showing which vehicles influence predictions

  • Performance metrics: RMSE, MAE, ADE (Average Displacement Error), FDE (Final Displacement Error)

  • Ablation study showing impact of attention mechanism and interaction modeling

  • Real-time inference capability demonstration (<100ms per prediction)

  • Unit tests for data preprocessing, model architecture, and prediction accuracy

Execution structure and Pre-requisite

  1. Pre-requisite: PyTorch/TensorFlow, NumPy, Pandas, Matplotlib, scikit-learn
  2. File Structure:
    (a) Main Model
    (b) Dataset Handling
    (c) LSTM, GRU, attention Modules
    (d) Training Loop
    (e) Testing and Metrics
  3. Dataset: NGSIM I-80/US-101
  4. Hyperparameters: Hidden Size, Number of Layers, Dropout rate, learning rate, Batch Size.
  5. Training Details: Epochs, Batch Size, Early Stopping Criteria
  6. Applications: Collision avoidance, Lane Change Prediction, Motion Planning
  7. Limitations: Assumes constant road structure, may struggle with rare maneuvers
  8. Future Work: Graph Neural Networks for better interaction modeling, multi-modal predictions, uncertainty quantification
  9. Computational Requirements: GPU recommended for training, CPU sufficient for inference.
Ngôn ngữ chính
Python
Star
1.9k
Fork
313
Merge trung bình
9 ngày 10 giờ
Pull request đã merge (30 ngày)
7

Chuẩn bị môi trường

Mở trong Codespaces

Khởi chạy dev container của dự án ngay trên trình duyệt, bằng tài khoản GitHub của bạn.

  • Có Dockerfile hoặc tệp Docker Compose
  • Không có mẫu pull request
  • Không có hướng dẫn đóng góp

Bắt đầu từ đâu

  1. Đọc hết issue, rồi đọc hướng dẫn đóng góp của dự án.
  2. Bình luận trên issue rằng bạn sẽ nhận — tránh hai người làm cùng một việc.
  3. Fork repository và làm thay đổi trên một nhánh.
  4. Mở pull request có tham chiếu số hiệu của issue.

Issue khác của ShisatoYano/AutonomousVehicleControlBeginnersGuide

Tất cả issue của ShisatoYano/AutonomousVehicleControlBeginnersGuide

Issue tương tự

Thêm issue về Python

Nhận issue mới trong hộp thư của bạn

Bản tóm tắt ngắn những issue GitHub phù hợp với người mới.