Joint Intent & Trajectory Prediction (MTITP GAN)

Joint Intent and Trajectory Prediction (MTITP) is the generative sequence modeling framework developed in Chapter 7 of my doctoral dissertation. Rather than treating intent recognition and trajectory forecasting as separate problems, MTITP couples them in a unified multi-task network.

Core Innovations & Architecture

• MTITP-WGAN Framework: Formulates a Multi-Task Intent and Trajectory Prediction Generative Adversarial Network trained with Wasserstein loss and gradient penalty (WGAN-GP) to eliminate mode collapse and generate realistic, multi-modal future paths.
• Three-Pronged Joint Output:
  1. Past-Intent Classification: Recognizes behavioral intent over historical encounter windows.
  2. Future-Intent Forecasting: Predicts forward-looking tactical intent transitions.
  3. Intent-Conditioned Trajectory Synthesis: Generates kinematically feasible future coordinate sequences conditioned on predicted intent.
• Robustness Under Sensor Noise: Evaluated extensively across simulator-generated benchmarks with varying noise regimes (Noiseless, Noisy 1, Noisy 2) and out-of-distribution adversarial encounters (herding, ramming, blocking).
• Ablation & Baselines: Rigorously benchmarked against MarITGAN v2 and non-adversarial variants (MTITP-L2) to isolate the exact contribution of adversarial loss to trajectory fidelity.

Forthcoming Publications

• Two pending journal manuscripts are currently derived from this framework:
  • Joint Multi-Task Intent and Trajectory Prediction for Autonomous Maritime Surface Vessels using Wasserstein GANs (Target: IEEE Transactions on Intelligent Transportation Systems (T-ITS))
  • Generative Scenario Augmentation and Counterfactual Intent Analysis in Safety-Critical Maritime Encounters