Driver assistance: Coming revolution

Adas software algorithms must account for road types, speed and threat complexity

 

The three major trends of electrification, connectivity and autonomy all have software in common, according to Thomas Gage and Jonathan Morris from Marconi Pacific

 

We are on the threshold of a radical change in vehicle technology. No, it’s not automation, although that will come very soon. Instead, change is being driven by the underlying technology for automation that is already here and advancing rapidly; that is, crash avoidance technology delivered by advanced driver assistance systems (adas).

Adas makes safety and marketing sense. Whether it is Daimler, Toyota, Ford, Nissan, GM, another vehicle OEM or even Google, none are going to put vehicles on the road that can steer, brake or accelerate autonomously without having confidence that the technology will work. Adas promises first to reduce accidents and assist drivers as a copilot before eventually taking over for them on some of and eventually all their journey as an autopilot.

As for how quickly the impacts of this technology will be felt, the adoption curves for any new technology look very similar to one another. For example, the first commercial mobile-phone network went live in the USA in 1983 in the Baltimore-Washington metropolitan area. At the time, phones cost about $3000 and subscribers were scarce. Even several years later, coverage was unavailable in most of the country outside of dense urban areas. Today, there are more mobile-phone subscriptions than there are people in the USA, and more than 300,000 mobile-phone towers connect the entire country. Low-end smartphones cost about $150.

Vehicle technology is moving forward at a similar pace. And, because transportation is so fundamental to how we live, the disruptive effects are likely to be astoundingly large.

 

Three vehicles

The development of automation and adas is not the first trend to upend the auto industry status quo. International competition and liberalised trade forever altered the automotive OEM landscape, eroding the US sales market share of the big three car makers from 72 to 45 per cent in the past 20 years. And while vehicle technology has advanced enormously, the basics of driving have not changed much in the past 40 years.

Now, every day in California’s South Bay, you can commonly see three vehicles representing three world-changing trends in the automotive industry – a sleek Tesla S rolling quietly past, a late-model sedan with an Uber U in the back window picking up a passenger and a heavily modified Lexus SUV with a spinning lidar on the roof, driving itself down the street while a Google employee (or an employee from an auto OEM in one of their vehicles in other parts of the world) collects data.

These daily sights represent three technology-driven trends that are simultaneously arriving to disrupt the automotive status quo: electrification, connectivity and autonomy. Each trend is moving at a different pace, but all three have one thing in common: it’s all about the software.

 

Software

Since 2004, the costs of electronics in an average vehicle have doubled from 20 to 40 per cent. Today’s luxury vehicles commonly contain 100 microprocessors and run 100 million lines of software code, controlling everything from engine timing to infotainment systems. We are now at an inflection point where software, sensors and processors are delivering entirely new areas of vehicle functionality, and not simply transitioning conventional functions from mechanical to electronic control. Both the adas of today and the autonomous driving systems of tomorrow will rely completely on software to make sense of a slew of data from sensors, cameras, the internet, infrastructure and other vehicles.

The increasing complexity of vehicles has already shifted the automotive value chain. The trends of electrification, connectivity and automation will only accelerate this shift in value toward those companies that create electronics and software, and away from OEMs that fail to innovate.

This shift will have two effects. First, software will become a critical market differentiator, pressuring OEMs to shorten product cycles and provide support and updates for legacy systems. To meet consumer demands for current technology, OEMs are now forced to modify significantly or introduce new models after only three or four years, while previous product cycles averaged five to eight years. This leaves OEMs with many challenges including rapid innovation, complex QA testing, higher development costs, less time to amortise R&D and the need for new sales and vehicle-ownership models.

Secondly, the shift to software allows new entrants to innovate in an industry with notoriously high barriers to entry. After decades of the same players dominating the industry, Google, Apple, Tesla and Uber are all poised to remake the automotive landscape through software, a thought that would have seemed highly unlikely even five years ago.

In a typical adas-equipped vehicle, applications such as forward collision avoidance (FCA) are enabled by a set of sensors that provide data on the external driving environment to an electronic control unit (ECU). This unit then uses software to determine whether a threat is present and operates brake actuators or, potentially, other countermeasures to mitigate the threat.

The sensors available today for driver assistance applications are the hardware foundation for autonomous vehicles. But tomorrow’s sensors will necessarily be smaller, faster and cheaper. For example, Continental’s sensors and processors can transmit and recalculate algorithms needed to understand the driving environment every 10 to 60ms, while the human brain can pass a message from a sensory neuron to a motor neuron in only a few milliseconds.

But the real gap between adas today and the fully autonomous systems of tomorrow is seen in software. Regardless of how fast inputs can be processed, the software algorithms that will allow vehicles to drive themselves more efficiently and safely than human drivers in complex driving environments remain the biggest challenge. Complexity is defined by both the number of threats, characterised by the types of threats that a driver can encounter on different road types – for example, pedestrians, vehicles traveling at a right angle to the vehicle, bicyclists – and the speed at which the vehicle is driving (see diagram).

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