A Multimodal Perception-Driven Self Evolving Autonomous Ground Vehicle

Jamie Roche

doi:10.1109/TCYB.2021.3113804

2022-09-01

Jamie Roche *

Varuna De-Silva

A.M. Kondoz

DOI：10.1109/TCYB.2021.3113804

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摘要

En 中文

Increasingly complex automated driving functions, specifically those associated with free space detection (FSD), are delegated to convolutional neural networks (CNNs). If the dataset used to train the network lacks diversity, modality, or sufficient quantities, the driver policy that controls the vehicle may induce safety risks. Although most autonomous ground vehicles (AGVs) perform well in structured surroundings, the need for human intervention significantly rises when presented with unstructured niche environments. To this end, we developed an AGV for seamless indoor and outdoor navigation to collect realistic multimodal data streams. We demonstrate one application of the AGV when applied to a self-evolving FSD framework that leverages online active machine-learning (ML) paradigms and sensor data fusion. In essence, the self-evolving AGV queries image data against a reliable data stream, ultrasound, before fusing the sensor data to improve robustness. We compare the proposed framework to one of the most prominent free space segmentation methods, DeepLabV3+ [1]. DeepLabV3+ [1] is a state-of-the-art semantic segmentation model composed of a CNN and an autodecoder. In consonance with the results, the proposed framework outperforms DeepLabV3+ [1]. The performance of the proposed framework is attributed to its ability to self-learn free space. This combination of online and active ML removes the need for large datasets typically required by a CNN. Moreover, this technique provides case-specific free space classifications based on the information gathered from the scenario at hand.

Keyword:

Robot sensing systems

Cameras

Convolutional neural networks

Convolution

Acoustics

Optical sensors

Space vehicles

Active learning

assistive robots

autonomous ground vehicles (AGVs)

autonomous vehicles