Abstract
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.
Highlights
IINHERENT complexities and diverse environments prevent autonomous ground vehicles (AGV)’s from being programmed with a fixed set of rules that govern the policy that controls them [2]
We demonstrate a self-evolving Free Space Detection (FSD) framework that self-learns using a combination of online and active machine learning (ML)
We chose online ML over other ML paradigms, like incremental ML, because it processes data as it becomes available in sequential order
Summary
IINHERENT complexities and diverse environments prevent AGV’s from being programmed with a fixed set of rules that govern the policy that controls them [2]. AGV’s need to learn to make decisions independently based on the scenarios they face and the objects perceived. In this manner can adequate driver policy be derived, where the AGV self-evolves over time, depending on the encounters they make. Regarded as one of the most fundamental elements of perception, FSD experiences a lack of research in unstructured niche environments [3]. There is a question about whether CNN’s can access diverse data quantities to achieve this task in practice. While results with large datasets are impressive, on a wide range of potential representations where data is sparse or lacks diversity, a CNN will always encounter difficulties [9]
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