Semantic Map Learning of Traffic Light to Lane Assignment based on
Motion Data
- URL: http://arxiv.org/abs/2309.14793v2
- Date: Thu, 28 Sep 2023 08:20:42 GMT
- Title: Semantic Map Learning of Traffic Light to Lane Assignment based on
Motion Data
- Authors: Thomas Monninger, Andreas Weber, Steffen Staab
- Abstract summary: Autonomous vehicles commonly rely on High Definition (HD) maps that contain information about the assignment of traffic lights to lanes.
To remedy these issues, our novel approach derives the assignments from traffic light states and the corresponding motion patterns of vehicle traffic.
Our publicly available API for the Lyft Level 5 dataset enables researchers to develop and evaluate their own approaches.
- Score: 12.853720506838043
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding which traffic light controls which lane is crucial to navigate
intersections safely. Autonomous vehicles commonly rely on High Definition (HD)
maps that contain information about the assignment of traffic lights to lanes.
The manual provisioning of this information is tedious, expensive, and not
scalable. To remedy these issues, our novel approach derives the assignments
from traffic light states and the corresponding motion patterns of vehicle
traffic. This works in an automated way and independently of the geometric
arrangement. We show the effectiveness of basic statistical approaches for this
task by implementing and evaluating a pattern-based contribution method. In
addition, our novel rejection method includes accompanying safety
considerations by leveraging statistical hypothesis testing. Finally, we
propose a dataset transformation to re-purpose available motion prediction
datasets for semantic map learning. Our publicly available API for the Lyft
Level 5 dataset enables researchers to develop and evaluate their own
approaches.
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