# Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network

arXiv: https://arxiv.org/abs/2409.18399 · HTML: https://mlsanity.com/paper/2409.18399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving](https://mlsanity.com/paper/1808.05819/) (2018)
- [Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks](https://mlsanity.com/paper/1807.09995/) (2018)
- [Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field](https://mlsanity.com/paper/1906.10307/) (2019)
- [A survey on robustness in trajectory prediction for autonomous vehicles](https://mlsanity.com/paper/2402.01397/) (2024)

## Abstract (arXiv)

Recently, the application of autonomous driving in open-pit mining has garnered increasing attention for achieving safe and efficient mineral transportation. Compared to urban structured roads, unstructured roads in mining sites have uneven boundaries and lack clearly defined lane markings. This leads to a lack of sufficient constraint information for predicting the trajectories of other human-driven vehicles, resulting in higher uncertainty in trajectory prediction problems. A method is proposed to predict multiple possible trajectories and their probabilities of the target vehicle. The surrounding environment and historical trajectories of the target vehicle are encoded as a rasterized image, which is used as input to our deep convolutional network to predict the target vehicle's multiple possible trajectories. The method underwent offline testing on a dataset specifically designed for autonomous driving scenarios in open-pit mining and was compared and evaluated against physics-based method. The open-source code and data are available at https://github.com/LLsxyc/mine_motion_prediction.git