# MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles

arXiv: https://arxiv.org/abs/2407.05811 · HTML: https://mlsanity.com/paper/2407.05811/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning](https://mlsanity.com/paper/2206.13387/) (2022)
- [MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction](https://mlsanity.com/paper/2201.07189/) (2022)
- [Map-Adaptive Goal-Based Trajectory Prediction](https://mlsanity.com/paper/2009.04450/) (2020)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Exploring Dynamic Context for Multi-path Trajectory Prediction](https://mlsanity.com/paper/2010.16267/) (2020)
- [Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety](https://mlsanity.com/paper/2307.05288/) (2023)
- [BEVSeg2TP: Surround View Camera Bird's-Eye-View Based Joint Vehicle Segmentation and Ego Vehicle Trajectory Prediction](https://mlsanity.com/paper/2312.13081/) (2023)
- [Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach](https://mlsanity.com/paper/2312.13104/) (2023)

## Abstract (arXiv)

Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering multiple possible future trajectories based on diverse sources of environmental data. In this approach, we leverage ResNet-50 to extract image features from high-definition map data and use IMU sensor data to calculate speed, acceleration, and yaw rate. A temporal probabilistic network is employed to compute potential trajectories, selecting the most accurate and highly probable trajectory paths. This method integrates HD map data to improve the robustness and reliability of trajectory predictions for autonomous vehicles.