Multi-modal Transformer Path Prediction for Autonomous Vehicle
- URL: http://arxiv.org/abs/2208.07256v1
- Date: Mon, 15 Aug 2022 15:09:26 GMT
- Title: Multi-modal Transformer Path Prediction for Autonomous Vehicle
- Authors: Chia Hong Tseng, Jie Zhang, Min-Te Sun, Kazuya Sakai, Wei-Shinn Ku
- Abstract summary: We propose a path prediction system named Multi-modal Transformer Path Prediction (MTPP) that aims to predict long-term future trajectory of target agents.
To achieve more accurate path prediction, the Transformer architecture is adopted in our model.
An extensive evaluation is conducted to show the efficacy of the proposed system using nuScene, a real-world trajectory forecasting dataset.
- Score: 15.729029675380083
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reasoning about vehicle path prediction is an essential and challenging
problem for the safe operation of autonomous driving systems. There exist many
research works for path prediction. However, most of them do not use lane
information and are not based on the Transformer architecture. By utilizing
different types of data collected from sensors equipped on the self-driving
vehicles, we propose a path prediction system named Multi-modal Transformer
Path Prediction (MTPP) that aims to predict long-term future trajectory of
target agents. To achieve more accurate path prediction, the Transformer
architecture is adopted in our model. To better utilize the lane information,
the lanes which are in opposite direction to target agent are not likely to be
taken by the target agent and are consequently filtered out. In addition,
consecutive lane chunks are combined to ensure the lane input to be long enough
for path prediction. An extensive evaluation is conducted to show the efficacy
of the proposed system using nuScene, a real-world trajectory forecasting
dataset.
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