The future of automotive AI is driven by a multitude of players with different expertise. Tier 1’s have traditionally been the leading integrators, but in an era where data has so much value, their position is challenged.
Automotive will see more disruption coming from startups that are hungry to change the status quo in the upcoming months. Mergers and acquisitions are also becoming more popular and strategic investments into Tier 2 companies that involve computer vision.
But this field requires consolidation on behalf of these new startup companies for both parties involved (Tier one’s and Startups) to offer a complete solution allowing them (Tier One) to sell compelling solutions.
Computer vision companies vary on a multitude of techniques and a combination of approaches. There is an excellent distinction between startups that look outside of the vehicle and those that look inside. But the overall issue remains the same, robustness, flexibility, and understanding of when to save lives.
Now the question is, why computer vision? As Elon musk said: “Startups that rely only on lidar and other radar techniques are doomed.” The reasoning is that we are trying to make a superior human perception, and by doing so, we can not omit vision. It is what makes us human.
The problem with startups in computer vision is that many compete with each other rather than collaborating, making the market more difficult to interact with each of these, and Integrators can not do the integration alone. Tesla being the single exception.
The key to solving this puzzle is how software and Hardware interact together, which can happen a lot because they are closely related. Moore’s law will end soon, and it was predicted that sometime in 2016, chips would stop getting exponentially faster every year.
The future chips will be more like the past chips, and it will take a lot longer to bring new developments in Hardware. But this doesn’t mean you can sit back and relax because there is plenty of work for everyone who knows how to get their hands dirty with programming languages or engineering. The most important thing is predicting what software problems that need solving-knowing which companies need skill sets before they have positions open for them means getting ahead on your game, so you’re not left out when these projects start rolling around. It’s time for startup consolidation with hardware technologies too.
The most complicated component of any system is the human. Computer vision software companies use techniques that complement what’s happening in hardware to get lots of passive data from the human body and interpret it accurately with machine learning so we can train agents to predict, within limits, what a user wants at any given moment based on their actions or inputted preferences and generate new content optimized for those desires. Hardware manufacturers are also developing newer technology like NPUs, which enable computers to process information faster by using less power than traditional processors while still handling more intricate tasks simultaneously, such as pattern recognition, all without relying on external servers for processing capabilities.
The output should be let us find the right partner and get to do the work. It sounds pretty straightforward when you think about how many complex processes need to work well together.
What’s promising about human-machine interfaces is that most of the underlying AI technologies, specialized chips, and sensors are predicted to improve dramatically over the next few years. And if consolidation happens fast, then one winning in this race will be with a compelling solution!