Design thinking is a process of finding innovative solutions to solving problems. It transforms the way we develop the products, strategy, and processes that are used in our day-to-day activities. This is a human-centered approach to what is technologically feasible and economically viable.
In this fast-paced world, multitasking has become a common way to save time—even while driving. Many road accidents take place where people lose their lives due to the distracted behavior of drivers. People behind the wheel need to avoid unsafe behaviors, such as talking to co-passengers, using mobile phones, and eating.
To combat the issue of distracted driving and minimize the number of accidents, the plan is to investigate how artificial intelligence (AI) could automatically detect the distracted activity of the drivers and, based on the type of distraction, alert them as a safety precaution. We envision this type of product being embedded in cars.
This use case explicates how a cross-functional team first delineates the problem and its parts and then how a developer solves this problem using AI.
This five-part series of articles explain the entire process of how such an AI-based product would be created from scratch, taking you through the steps of conceptualization, research and development, and finally productization. While we give a sense of the entire process here, we take advantage of Intel® AI technology to emphasize the AI research and software-development part of the process.
Forming a cross-functional team is quintessential to rapidly building a complex product or solution.
Most AI products are complex and require significant financial, legal, deployment, and design considerations. This complexity makes it important to have a cross-functional team that enables quick ideation and decision making. The team needs to come together with a big hairy audacious goal (BHAG) attitude so as to promote the team’s initiative rather than an individual member. Enable initiatives carved by the team, rather than slice them to the bare minimum.
Figure 1. Cross-functional team
During conceptualization, the cross-functional team generally has decision-making representatives from Sales, Marketing, Product Design, Finance, Legal, and Research and Development, as illustrated above. Commitment to the goals of the project is important for this team to conceptualize a viable product.
The project sponsor (generally senior management) requests the allocation of the team along with signed goals that are committed to by the leaders of all the major divisions/departments in the firm, as illustrated above. The leader then assigns someone to work on this project full-time.
The development team requires experience with the following:
A basic understanding of client-server architecture is an added advantage.
The problem that we are trying to solve—changing distracted-driving behavior—is of interest to the following industries:
To solve complex problems, the design-thinking process, which alternates between divergent and convergent modes, has five stages: discovery, interpretation, ideation, experimentation, and evolution.
In this stage, we try to empathize with the problems and needs of drivers. Everyone in the team has to understand, observe, listen, and manage customer expectations, experiences, and motivations. The information gathered in this stage will be used in the next stage to define the development of the product.
During this stage, we collected a set of characteristics for the behavior-enhancement system:
The ideas collected in the discovery stage are now converted into meaningful insights. Here, most of the questions are “what” questions. The team is engaged in storytelling based on the insights. Then, the designer team uses the storytelling to gather ideas for establishing features, functions, and other elements that allow them to solve the problems.
Here, since all the participants in the team were drivers, we discussed what the distractions were and how much they affected the driving. Statistics of how many accidents do happen because of driving were also discussed.
At the end of this stage, we identified the following features:
The goal of this process is to develop fresh ideas from the diverse group of people. This process lasts for an hour, during which people generate ideas without any constraints. Many ideation techniques—such as brainstorming, brain writing, worst possible idea, and SCAMPER—are used to identify possible solutions to the problem. After the ideas are generated, the team selects a few promising and exciting ideas to use in the final product.
We selected the following ideas:
All the above ideas were collected and put together in a document to be addressed in the next stage for feasibility.
At this stage, the design team debates and critiques all the solutions provided and selects the one that has minimum viable product and feasibility. Prototypes are shared and tested within and outside the team. Based on the user experiences, the prototypes are investigated and either accepted, improved, re-examined, or rejected. At the end of this phase, the design team has a better idea of how drivers think, feel, and behave while interacting with the end product.
During this iterative process, the best solutions identified during the experimentation phase are rigorously tested by the designers and drivers. During testing, people give feedback on how they feel, what they think, and how they understand the product. Many improvements and alterations are made to meet and exceed people’s goals.
The final concept narrative looked something like this:
This narrative, in addition to other idea charts and illustrations voted to be the best, would be then stored as a final concept.
Figure 2. Idea chart of final concept
The development team is given the following assets:
The second article of this Combating Distracted-Driver Behavior series, Experimental Design and Data Preparation for a Distracted-Driver AI Project, covers how research and development helps you to build your project. It mainly discusses how to prepare a dataset, how to approach a solution, and how to create a topology and design for an experiment.
Part 1: Overview of a Use Case: Combating Distracted-Driver Behavior
Part 2: Experimental Design and Data Preparation for a Distracted-Driver AI Project
Part 3: Training and Evaluation of a Distracted-Driver AI Model
Part 4: Designing and Fine-tuning a Distracted-Driver AI Model
Part 5: Overview of Productization for This AI Project
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