In UX research, it is crucial to build products with a clear objective that meets a specific need of a well-defined target audience. However, within the context of self-driving cars (SDCs) research, this principle appears to be largely neglected.
SDC research seems to have the goal of creating cars that can drive anywhere, at any time, with anyone, which is the opposite of a clear objective that meets a specific need of a well-defined target audience.
The issue with this approach is that data scientists create cars that work well in general, but fail once being tested in real world conditions, such as safely navigating jaywalkers. This lack of consideration for the target audience and their environment can lead to disastrous consequences, including accidents and loss of consumer trust.
While failure during testing is normal and even desirable, it is essential to anticipate predictable failures during the design phase. Data scientists can save time and resources by learning about their target audience and their environment to find clues about such failures.
To maximize the effectiveness of self-driving car development, data scientists should prioritize building from clear goals that reflect the target audiences’ real-world context. This approach allows them to focus on critical problems that are most relevant to the actual use of the vehicle.
So, to avoid the costly setbacks of going back to the drawing board, data scientists need to invest time in getting to know the target audience and their environment. This proactive approach allows you to design and develop self-driving cars more efficiently, saving time and resources in the long run.