3) we will develop a quantitative bicyclist model that could predict bicyclists’ behaviors, such as sudden swerving to left, by using the previous information of the bicyclist, traffic, and road environment.Įmbedded General-Purpose Cognitive Computing for Sensory Processing 2) we will develop bicycle-related corner cases using an existing driving dataset from a large scale naturalistic driving study – Safety Pilot Model Deployment (SPMD), which was conducted by the University of Michigan Transportation Research Institute (UMTRI). This proposed work has three components: 1) we will examine the bicycle-related crash reports from the available crash databases (e.g., Michigan Police crash reports, FARS, and GES). In this project we will develop bicycle-related corner case scenarios and a bicyclist behavioral model for testing self-driving cars using existing large-scale naturalistic driving data and crash data. The key insight of our approach is that task-oriented active perception allows us to probe only the task-relevant parts or properties of the environment, avoiding the complexity of fully-segmenting and registering all objects.ĭeveloping Bicycle-Related Corner Case Scenarios and a Bicyclist Model for Testing Self-Driving Cars Using Naturalistic Driving Data and Crash Data Instead of the perceive-then-act approach, we propose to solve the problem of manipulating heterogeneous objects in dense clutter through task-oriented active perception and manipulation. The standard perceive-then-act approach, which registers CAD models of objects to sensor data before planning to manipulate, is impractical in such densely cluttered scenarios as some objects may be unknown, some can change shape, and many will be partially or fully occluded. These objects vary in size, from small cups to large chairs they may vary in structure, from rigid utensils to articulated home gadgets like can openers, even to fully deformable objects like clothing and blankets they may be composed, such as stacks of cups they may commingle, such as a collection of plates, glasses and utensils on a tray. To be effective for these kinds of tasks a robot must be able to perceive and manipulate various types of objects in dense clutter. We envision domestic assistance robots that are capable of performing many practical tasks, such as cooking, cleaning, and laundry, for elderly or disabled people. Finally, I decided to integrate with Spacy, since training a custom Spacy TextCategorizer seems like a lot of hassle if you want something quick and dirty.Task-Oriented Active Perception and Motion Planning for Manipulating Piles of Stuff Additionally, it made sense to integrate sentence-transformers and Hugginface zero-shot, instead of default word embeddings. Rasa NLU has a nice approach for this, but its too embedded in their codebase for easy usage outside of Rasa/chatbots. Huggingface does offer some nice models for few/zero-shot classification, but these are not tailored to multi-lingual approaches. Import spacy import classy_classification data = ] Credits Inspiration Drawn From
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