I time traveled last week and I have returned to describe the wonders of the future. My time machine was not a DeLorean (as in the Back to the Future film series), it was an autonomous, electrically powered pod, a silver and blue device that hummed along calmly, and deposited me at earth coordinates 44°53'31.6"N 0°33'58.4"W, the doorstep of the Intelligent Transport Systems (ITS) World Congress. I’m not sure in what year I landed. I would guess 2020 or thereabouts. I did not time travel alone. There were many others.
Today’s automobiles have as much in common with advanced consumer electronics as they do transportation. In fact, according to Car & Driver magazine, your car is the most advanced electronic device you own, with high-end luxury vehicles typically sporting more than 100 electronic control units (ECUs).
Safe and reliable operation of the vehicle is, of course, the primary objective during electronic systems confirmation testing - But there is more at stake here for automotive manufacturers. A subset of a vehicle’s computing power directly affects its drivability – its vehicle dynamics fingerprint and subjective character, which are crucial to brand identity and value perception. Over many years, companies like Ford and BMW (“Sheer Driving Pleasure”) have anchored their automotive brand identity on the way their cars drive. For these and many other manufacturers it is crucial that the myriad electronic systems placed between the driver and the road do not detract from the actual driving experience.
Advanced Driver Assistance Systems (ADAS) implementations are on the rise as vehicle manufacturers continue to introduce greater levels of supplemental control into the driving experience, with greater safety and convenience as the goals.
Early testing in a digital environment is just as applicable to ADAS development as it is to everything else on board – perhaps even more so. Consider the case of introducing a new ADAS function such as a lane departure warning system. The system may be unfamiliar to drivers, thus making their reactions harder to predict. It is certainly desirable to connect real drivers with such a system as early as possible. How can this be achieved?
At the heart of any engineering class Driver-in-the-Loop (DIL) simulator is a vehicle physics model, a mathematical representation of the systems of the driven car.
Different DIL experiments often require different types of vehicle models. Sometimes this is due to the desire for computational efficiency – i.e. Real-time calculation resources can be allocated to the vehicle subsystem that is of interest for the particular DIL experiment.
It can be agreed without debate that tuning and developing cars requires both subjective and objective assessments. Subjective information typically comes from experienced drivers with an ability to meaningfully describe vehicle behavior. Objective information typically comes from off-line simulations, on-car sensors and competent data acquisition and analysis. How to weigh all this information and mold it into a vehicle identity is another (big) topic altogether – so for the moment, let’s focus on the information itself, and more specifically address this question: Is it possible to gather both subjective and objective measurements in driving simulators? The answer is a qualified “yes.”
As noted in previous articles, automotive driving simulators can take on many forms, ranging from consumer-oriented entertainment devices to industry-oriented engineering tools. The question posed here is this: If we are interested in the engineering tool end of the spectrum, is it possible to puzzle out some of the defining characteristics of a “good” automotive driving simulator?
In order to succeed in the marketplace, vehicle constructors strive to create cars that are appealing to the senses of prospective buyers, and deliver the correct “message” -- that careful mix of brand identity and fulfilment of target market requirements. As such, cars are never created in a vacuum; vehicle designers need to touch them, see them, and drive them as they are being developed and grant many subjective acceptance nods before releasing them into the wild. Atop this, vehicle designers must satisfy a large number of objective obligations, many of which are related to the simultaneous (and conflicting) pressures of reduced time-to-market and increased vehicle complexity and regulatory compliance. One key to success for the design and development of modern automobiles is placing real drivers into early and often contact with virtual vehicles, sub-systems and environments/scenarios via Driver-in-the-Loop (DIL) simulation. Let’s explore some specific DIL use cases.
Motion sickness has been a significant issue for automotive driving simulators since they were first introduced. This is because the very simulator systems that can provide a driver with useful motion and visual feedback also have the potential to violate a driver’s expectations, causing disorientation and discomfort. While motion sickness can be excused as a mere annoyance with entertainment class driving simulators, it is generally regarded as unacceptable for engineering class Driver-in-the-Loop (DIL) simulators, such as those used by professionals to evaluate vehicle and automotive subsystem designs.
The process of designing and testing automobiles and their components has grown exponentially complex and expensive as fundamental vehicle sub-systems such as powertrain, steering, and braking systems have evolved from purely mechanical implementations into sophisticated electromechanical ones. Today, the simulation tools that are used to model and analyse automobiles are equally complex and costly.. This is particularly true in the case of driving simulators where human drivers are interacting in real time with sophisticated virtual reality models. So what might be the cost of a modern driving simulator?
There is a broad spectrum of Driver-in-the-Loop (DIL) simulators in use in the world today – Ranging from low cost gaming-oriented devices to multi-million dollar simulators that can fill up an entire room. If we are interested in DIL simulators for their potential to assist with particular areas of vehicle development or driver assessment, we might be interested in certain types of simulators, but not others. For example, if our interest is in finding a tool primarily to help develop Electric Power Assisted Steering (EPAS) or Electronic Stability Control (ESC) systems, we may find that certain types of DIL simulators are better suited due to the technology employed or fundamental performance capabilities.