Some might assert that autonomous personal vehicles are being developed to improve safety, reduce fuel consumption, decrease urban infrastructure strains, and so on.  But let’s be honest: One of the key economic “drives” for this technology, no pun intended, is related to the prospect that people will have new time windows in their days in which to consume visual media.  However, due to some fundamental aspects of human physiology, this lucrative ambition may prove to be more difficult to implement than imagined.

In an age when so many vehicle sub-systems and components can be verified and signed off virtually – via off-line computer modeling, virtual test driving in Driver-in-the-Loop (DIL) simulators, and so on – it’s ironic that the one component that arguably has the biggest impact on overall vehicle performance continues to remain just out of reach for computational analysists: The tire.

Self-driving cars are the new black. Every OEM and major tech company is taking on the autonomous challenge, and the competition is fierce. 

As an OEM or Tier 1 supplier, the landscape is rife with offerings, software tools that are aimed at assisting all areas of autonomous vehicle development and validation, many of them pioneering grabs at what is promising to be the Wild West of automotive engineering in the coming years.  And yes, some of these tools are either direct or indirect products of the gaming industry. 

If anyone asked the question, “Would you trust your autonomous vehicle validation to a game?” most of us would cringe a little.  But such questions are actually not too far off the mark these days. 

Customer race cars are a growing market for major automotive manufacturers. GT3 racing in particular continues to attract new manufacturers. Lexus (RC F), Acura (NSX) and Mercedes-AMG (GT) are joining the competitive IMSA GT Daytona class here in 2017. Ford meanwhile is the latest company to join GT4 competition on both sides of the Atlantic, having unveiled the Mustang GT4 at the SEMA show in November.

In the early stages of specifying and deploying any new Driver-in-the-Loop (DIL) simulator, one of the most common considerations is the integration with 3rd party hardware and software tools.  For example, you may already have an in-house solution for vehicle physics simulation, or you may have existing Hardware-in-the-Loop (HIL) test benches that must be connected to your DIL simulator.

Can a “turn-key” DIL simulator that is, by default, an all-inclusive offering, give you the seamless connectivity with your preferred tools that you need?  Well, sometimes yes, and sometimes no – and finding this out with certainty involves looking beyond a rote specification summary.

In case you've happened to miss it, advances in computer graphics have elevated video gaming to a whole new level of realism in recent years. Witness iRacing and rFactor as two of many examples of the new level of "ultra-realism."  If we make the assertion that games can be classified as a type of simulation, vehicle engineers might be left to wonder if the gamers are somehow winning the race, figuratively speaking.  There is no need to worry.

Thanks to new LIDAR scanning technologies and sophisticated scene and surface rendering offerings from entities such as rFpro (not to be confused with rFactor), the capability already exists to realistically recreate geo-specific locations that are applicable to the engineering-class virtual test drive world – the world that is of interest to carmakers.

Broadly speaking, today’s vehicles have significantly shorter production runs than the enduring products of yore. To wit: Volkswagen’s Beetle was manufactured for approximately 33 years.  Some might say 65 years, but that’s another story…

Are there any recent examples of such long-lived model runs?  Well, in 2014 the first-generation Volvo XC90 reached the end of a 12-year production run – which is certainly impressive. But any car that survives a decade would be an exception to the rule in the auto industry these days.  Let’s face it:  Modern carmakers have the arduous task of designing, developing, and deploying new products and major model updates at a tremendous pace.

Side-stepping entertainment applications, automotive driving simulators have historically been purposed almost exclusively towards human behavioral studies – and to good end. Inside the safe, controlled confines of a driving simulator lab, researchers can, for example, assess a driver’s performance while impaired from alcohol consumption, or explore the implications of driver distraction.

Laboratories such as NHTSA’s National Advanced Driving Simulator (NADS) at the University of Iowa in the USA have been used for countless studies that have directly contributed to improvements in highway safety. Furthermore, some automotive manufacturers use driving simulators to observe and/or survey typical drivers’ interactions with infotainment systems and ADAS interventions.

The concept of Marginal Gains is not new, nor is it particularly revolutionary. In fact, at first glance, it is so self-evident and logical, that it hardly seems worthy of special nomenclature at all.

The crux of the idea is that if you break down every aspect of what you are trying to achieve into key areas and improve each area by just a little, say 1%, then the combined improvement  – and the total achievement – can be significant.

In Pursuit of Speed

The concept is a variant of the ancient Chinese proverb attributed to Lao Tzu: “A journey of 1,000 miles begins with a single step.” In more recent times, the concept’s application was popularised in competitive sports by Sir Dave Brailsford, the man charged with transforming British Cycling race participants into race winners. And, of course, Marginal Gains is a motorsports maxim, even when it is not called out by name.

If one needs an illustration of the sensor complexity that is arising from ADAS-capable vehicles evolving into new autonomous and semi-autonomous modes of transport, look no further than the highly automated Delphi Drive concept vehicle. External awareness sensors such as radar, LiDAR, and cameras are just the tip of the iceberg for this vehicle. On board one can find touch sensors on the steering wheel, driver-facing cameras in the A-pillars, a fingerprint reader in console, and logic-activated haptic feedback motors in the seats.

With its myriad sensors, and feedback devices, and information processing strategies, and so on, this highly modified Audi SQ5 is in use now as a rolling test bed and demonstration platform for all manner of human-to-vehicle and vehicle-to-infrastructure (V2X) possibilities. Oddly, this test bed of today may be evidence that we are not far from a time when even “normal” production vehicles carry this level of sensing capability.