Investigating the Effect of Sensor Modalities in Multi-Sensor
Detection-Prediction Models
- URL: http://arxiv.org/abs/2101.03279v1
- Date: Sat, 9 Jan 2021 03:21:36 GMT
- Title: Investigating the Effect of Sensor Modalities in Multi-Sensor
Detection-Prediction Models
- Authors: Abhishek Mohta, Fang-Chieh Chou, Brian C. Becker, Carlos
Vallespi-Gonzalez, Nemanja Djuric
- Abstract summary: We focus on the contribution of sensor modalities towards the model performance.
In addition, we investigate the use of sensor dropout to mitigate the above-mentioned issues.
- Score: 8.354898936252516
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Detection of surrounding objects and their motion prediction are critical
components of a self-driving system. Recently proposed models that jointly
address these tasks rely on a number of sensors to achieve state-of-the-art
performance. However, this increases system complexity and may result in a
brittle model that overfits to any single sensor modality while ignoring
others, leading to reduced generalization. We focus on this important problem
and analyze the contribution of sensor modalities towards the model
performance. In addition, we investigate the use of sensor dropout to mitigate
the above-mentioned issues, leading to a more robust, better-performing model
on real-world driving data.
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