Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
This research addresses the problem of improving the robustness and generalization of multi-sensor detection-prediction models for self-driving cars, which is an incremental improvement for autonomous vehicle developers.
This paper investigates the contribution of different sensor modalities to the performance of multi-sensor detection-prediction models in self-driving systems. They also explore sensor dropout as a method to improve model robustness and performance on real-world driving data.
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.