Summary of Multimodal Trajectory Prediction For Autonomous Driving on Unstructured Roads Using Deep Convolutional Network, by Lei Li et al.
Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network
by Lei Li, Zhifa Chen, Jian Wang, Bin Zhou, Guizhen Yu, Xiaoxuan Chen
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method predicts multiple possible trajectories and their probabilities for a target vehicle in open-pit mining scenarios. The approach encodes the surrounding environment and historical trajectories of the target vehicle as a rasterized image, which is used as input to a deep convolutional network. The model underwent offline testing on a dataset designed specifically for autonomous driving in open-pit mining, outperforming physics-based methods. This paper contributes to the development of safe and efficient mineral transportation systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team developed a new way to predict where vehicles might go in an open-pit mine. They used special images that show what’s happening around the vehicle, like other cars or obstacles. Then they trained a computer model to look at these images and predict where the vehicle might go next. The model was tested on a special dataset just for this kind of situation and did better than a different type of model. This could help make mining safer and more efficient. |
Keywords
» Artificial intelligence » Convolutional network
