The auto industry has made significant headway with developing and trialing autonomous prototypes, particularly in the U.S., and is starting to hone the sensor arrays, software stacks and algorithms, and understanding of the level of computing required to process the information for the vehicle to make the right decisions in every scenario. For example, in California, the autonomous vehicle companies must provide a report every year showing the number of vehicles, number of miles driven and the number of disengagements of the autonomous system as it hands back control to the human safety driver.
We are seeing many companies from ride-sharing pioneers, technology giants, Silicon Valley start-ups, and the traditional automotive OEMs and tier 1s, all putting their technology on our roads, fuelled by big investments and big expectations.
However, these are still prototypes and are some way from real deployability as the computing systems are too big, too power hungry, too costly, and thermally inappropriate. The other challenge is that the software stack is still struggling to deal with all corner cases, and making a fully autonomous car functionally safe through the all sensing, perception, decision, and driving phases is very challenging which requires there to be a safety driver in attendance at all times (which is not very deployable).
Over the last few years, we’ve started to see an interesting shift in the industry where OEMs and autonomous technology providers have broken the chain of command by now partnering with each other and discussing these issues directly with silicon tech companies, like Arm, to find out exactly what it is they need to implement in their designs and get from prototype to deployment. Recently announced, the Autonomous Vehicle Computing Consortium (AVCC) is an example of industry collaboration focused on deployable autonomous compute.