Key concept: Setting mental models can help users understand how to interact with products that adapt over time. This chapter covers:
Identifying existing mental models
Onboarding in stages
Planning for co-learning
Accounting for user expectations of human-like interaction
Key concept: To build effective mental models of AI-powered products, consider what you want people to know about your product before their first use, how to explain its features, and when it will need feedback from them to improve.
Identifying existing mental models
Onboarding in stages
Planning for co-learning
Accounting for user expectations of human-like interaction
Key concept: To build effective mental models of AI-powered products, consider what you want people to know about your product before their first use, how to explain its features, and when it will need feedback from them to improve.
oday, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior reflex approaches that directly map an input image to a driving action by a regressor. In this paper, we propose a third paradigm: a direct perception based approach to estimate the affordance for driving. We propose to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving. Our representation provides a set of compact yet complete descriptions of the scene to enable a simple controller to drive autonomously. Falling in between the two extremes of mediated perception and behavior reflex, we argue that our direct perception representation provides the right level of abstraction. To demonstrate this, we train a deep Convolutional Neural Network (CNN) using 12 hours of human driving in a video game and show that our model can work well to drive a car in a very diverse set of virtual environments. Finally, we also train another CNN for car distance estimation on the KITTI dataset, results show that the direct perception approach can generalize well to real driving images.
