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.
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.
These are still the early days for the human-machine interfaces (HMI) that will be crucial to the success of autonomous vehicles. And it’s unclear whether cross-industry standards will emerge regarding the application of autonomous technologies – not just in terms of functionality, but in the way that conceptual HMIs convey functionality to drivers / occupants.
Recent accident data leaves little doubt that safety will be one of the justifications for developing autonomous vehicle technologies in the coming years. In 2015, 2,348 more people – for a total of 35,092 – died in US traffic crashes, a 7.2% increase from the previous year that ends a five-decade trend of declining fatalities. Simultaneously, the White House and US Department of Transportation issued a call to action to scientists and public health experts to do more to prevent road traffic deaths.
With some studies estimating that autonomous vehicle technologies could reduce the number of accidents by as much as 90%, this rapidly emerging field is sure to remain a leading destination for research dollars.
Like any athletes, race car drivers have become known for their superstitions and pre-race rituals. Stirling Moss favored lucky #7 on his cars, Michael Schumacher odd numbers in general. Dale Earnhardt diligently avoided $50 bills and peanut shells, as do many NASCAR drivers now. Juan Pablo Montoya gets into his cars from the same side every time. Perhaps most infamously, Alberto Ascari died during an impromptu test of a Ferrari at Monza in 1955…having left his lucky blue helmet at home that particular day.
In recent decades there has been a decided shift in the way new cars are introduced into the marketplace. Compression of time-to-market cycles has been unprecedented; auto manufacturers are designing, developing, testing, refining, approving, and deploying vehicles into the world faster than ever before. And this is occurring simultaneously with the uptake and integration of staggering engineering-level complexities in the form of Vehicle-to-Everything (V2X) systems as well as on-board technologies such as Advanced Driver Assistance Systems (ADAS).
An economist might call this scenario a "problem" in the classic economic sense, i.e., one of the means of production (the available time to design and test a new vehicle) is changing and so too is at least one of the ends of production (the definition of what defines a "good car"). So the question becomes: What can be done to help solve this "problem?"