Multi-modal Scene-compliant User Intention Estimation for Navigation

• URL: http://arxiv.org/abs/2106.06920v1
• Date: Sun, 13 Jun 2021 05:11:33 GMT
• Title: Multi-modal Scene-compliant User Intention Estimation for Navigation
• Authors: Kavindie Katuwandeniya, Stefan H. Kiss, Lei Shi, and Jaime Valls Miro
• Abstract summary: A framework to generated user intention distributions when operating a mobile vehicle is proposed in this work. The model learns from past observed trajectories and leverages traversability information derived from the visual surroundings. Experiments were conducted on a dataset collected with a custom wheelchair model built onto the open-source urban driving simulator CARLA.
• Score: 1.9117798322548485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A multi-modal framework to generated user intention distributions when operating a mobile vehicle is proposed in this work. The model learns from past observed trajectories and leverages traversability information derived from the visual surroundings to produce a set of future trajectories, suitable to be directly embedded into a perception-action shared control strategy on a mobile agent, or as a safety layer to supervise the prudent operation of the vehicle. We base our solution on a conditional Generative Adversarial Network with Long-Short Term Memory cells to capture trajectory distributions conditioned on past trajectories, further fused with traversability probabilities derived from visual segmentation with a Convolutional Neural Network. The proposed data-driven framework results in a significant reduction in error of the predicted trajectories (versus the ground truth) from comparable strategies in the literature (e.g. Social-GAN) that fail to account for information other than the agent's past history. Experiments were conducted on a dataset collected with a custom wheelchair model built onto the open-source urban driving simulator CARLA, proving also that the proposed framework can be used with a small, un-annotated dataset.

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