# TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation

arXiv: https://arxiv.org/abs/2101.06374 · HTML: https://mlsanity.com/paper/2101.06374/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2021.

## Built on by (most cited first, 5 of 5)

- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [UTD-Yolov5: A Real-time Underwater Targets Detection Method based on Attention Improved YOLOv5](https://mlsanity.com/paper/2207.00837/) (2022)
- [Deep learning for Lagrangian drift simulation at the sea surface](https://mlsanity.com/paper/2211.09818/) (2022)
- [Online Lane Graph Extraction from Onboard Video](https://mlsanity.com/paper/2304.00930/) (2023)
- [Anchor-Intermediate Detector: Decoupling and Coupling Bounding Boxes for Accurate Object Detection](https://mlsanity.com/paper/2310.05666/) (2023)

## Abstract (arXiv)

In recent years, various state of the art autonomous vehicle systems and architectures have been introduced. These methods include planners that depend on high-definition (HD) maps and models that learn an autonomous agent's controls in an end-to-end fashion. While end-to-end models are geared towards solving the scalability constraints from HD maps, they do not generalize for different vehicles and sensor configurations. To address these shortcomings, we introduce an approach that leverages lightweight map representations, explicitly enforcing geometric constraints, and learns feasible trajectories using a conditional generative model. Additional contributions include a new dataset that is used to verify our proposed models quantitatively. The results indicate low relative errors that can potentially translate to traversable trajectories. The dataset created as part of this work has been made available online.