---
title: How to Stay in Touch with Connected Cars
description: These are still the early days for the human-machine interfaces (HMI) that will be crucial to the success of autonomous vehicles.
image: https://www.ansiblemotion.com/hubfs/Blog_Images/connected-cars-simulation-bw.jpg
---

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September 29, 2016 | [**Phil Morse**](https://www.ansiblemotion.com/automotive-driver-in-the-loop-simulation-articles/author/phil-morse)

# How to Stay in Touch with Connected Cars

![](https://www.ansiblemotion.com/hubfs/Blog_Images/connected-cars-simulation-bw.jpg)

#### Listen to the Blog Post

How to Stay in Touch with Connected Cars

4:49

These are still the early days for the [human-machine interfaces](https://en.wikipedia.org/wiki/User_interface) (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.

Meanwhile, today’s ADAS technologies are beginning to inform automobile drivers of the safety benefits that new developments can bring, as well as helping them to become accustomed to the feeling of a car that is momentarily taking control. But are we, ourselves, running the risk of becoming disconnected from “connected” vehicles?

## The Human Element

Within the automotive industry the latest generation of [Driver-in-the-Loop (DIL) simulators](https://www.ansiblemotion.com/driving-simulator-products) provides a mechanism for exploring the intricate relationship between drivers / occupants with piloted and/or conveyance vehicles. This is accomplished by creating realistic and immersive human surroundings inside a laboratory – including visual, acoustic, haptic and tactile, and motion cues such as ride frequencies and vehicle dynamic inputs – all the feedbacks that allow real people to interact realistically with virtual vehicles and imagined systems.

These imagined vehicle concepts can then be placed into geo-specific or representative environments wherein everything imaginable can be explored – from roadway hazards to interactions with other intelligent objects such as cars, pedestrians, and advanced infrastructure elements.  Not only does this provide a safe environment for exploring what might be otherwise considered dangerous scenarios, but it also provides a controlled, repeatable observation platform from which to gather objective measurements on human and vehicle behaviors.

 

*Connected Car speaks with Ansible Motion about connecting people with cars*

 

Using DIL simulators, the feedback loop of real people reacting to technologies – which are, in turn, responding to people's reactions – can be examined in a way that’s impossible to gauge accurately with Software-in-the-Loop (SIL) or [Hardware-in-the-Loop (HIL)](https://www.ansiblemotion.com/ansible-motion-automotive-driver-in-the-loop-simulation-news/right-on-cue-human-and-hardware-in-the-loop-simulation-h2il) testing alone.  In a sense, DIL simulation is a mechanism whereby the human touch can be brought back into the mix of core vehicle developments.

## **Who’s in Charge?**

One example is in the handover of (pilot task) control. A handover from human to an artificial intelligence can create some problematic ‘corner cases’ near the edges of normal parameter mapping, but the switch from driverless control back to the human can sometimes present an even bigger challenge. What is the cognitive state of the human? Is he or she paying attention and/or ready to resume the (piloting) task? What if the driver is not yet ready to be a driver again when handover initiates? All of these possibilities must be studied.

Another example is the case where a (human) driver assumes that he or she is in full control, but an assistive or semi-autonomous system assumes that some intervention is required. This could range from a very subtle interference, to a very direct, counterintuitive one, perhaps based on a logical derivative of a known safety case. If the augmentation assists the human driver in a way that is startling or unexpected, how does the human then react? Will the startled response produce statistically measurable reactions from human drivers that can be used to inform the AI system? If so, a DIL simulator lab might be the only practical way to collect enough information to make informed system / controller logic decisions and test their effectiveness in advance of market deployment.

## **Practice Makes Perfect**

In this and many other cases a controlled, repeatable laboratory environment that engages real people in the experimentation process becomes the ideal place to perform exploratory and comparative studies. Scenarios and conditions (both environmental and vehicular) can be created and modified as needed in order to gain statistical confidence in what would otherwise be a staggering array of possibilities. And no matter what the outcome of a particular experiment – good or bad, life-saving intervention or (virtual) collision, incident-inducer or scare – the test has been conducted in total safety via DIL simulation.  An experiment ends calmly, and man and machine are ready to have another go, in search of solutions that will keep us connected – while making sure that we are actually staying in touch.

To learn more about how engineering class Driver-in-the-Loop (DIL) simulators are emerging as a primary vehicle development tool for the cars of tomorrow, download our FREE eBook, [ Beyond the driver: Designing tomorrow's ADAS and CAV vehicles](https://www.ansiblemotion.com/beyond-the-driver-designing-tomorrows-adas-and-cav-vehicles):

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"name": "Connected Car talks Driver in the Loop simulators with Ansible Motion",
"description": "Interview about the application of driver-in-the-loop-simulators and autonomous cars",
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"transcript": "Host: Okay, tell us little bit about the background to Ansible Motion. And then we can get into some of the applications that you think are relevant for the manufacturers that are developing their connected cars. Kia: Ansible Motion started in 2009. We created it with the expressed intent of building, designing and deploying driver in the loop simulators. These do exist but we intended to create a unique pattern simulator and one that would inform the drivers of vestibular system and all of the other senses-the driving experience. In such a way that you can rely on their feedback. So, you could place expert drivers. Up to then pretty much all of the drivers in the loop simulators were only good for low to medium skilled drivers. So, if you put expert drivers in them you cannot gain any further utility from them. So, we looked at the problem of initially the human vestibular and we looked at the machinery that was out there to do that. We came up with a completely different design pattern for this and a completely different approach. We implemented that. It was very successful. Since creating that initial pattern of strata-form, platform, we have gone on and let up all the other different stimulants. So, we can now provide an extremely realistic and immersive driving environment from vision to audio to haptic to tactile to write frequencies to inputs. And we can create a laboratory like environment for a human driver, a skilled human driver to drive a virtual model and interact with it in a highly realistic and useful manner for engineering the vehicle and subsystems. Host: Okay, well as you well know we are all about development in connected car. How are the manufacturers using this type of simulator today to assist them? Kia: Really in any environment or any use case where human interaction with assistive technology or assistive technology Uh-mm… the interaction of that with a human. And, there's a feedback loop between these. The humans reacting to the technology which is reacting to the human. You cannot really have any competence in system performance if you've got that feedback loop without first trying it with humans. The idea is that now you have software in the loop and hardware in the loop. You have all of these different ways of testing a system and connected systems but until you've put human in the loop with those you-can't really confirm that the system is going to behave in a way that you wanted to. So, essentially all these cases are-opening up with the ability to do drive in the loop. Examples include things like-the classical one is adaptive handover. Host: Right. Kia: So, human-less driving. Adaptive system takes over that is not so bad. There are quite a few corner cases where that could be problematic. Adaptive assistance driving, when it wants to take over is a much bigger problem. What is the cognitive state of the human? Are they paying attention? Are they ready to be handed over? What if they are not ready? What if they are half ready? You have to study these cases until we have complete door-to-door with no possibility of human intervention. We are a long way off from that. So, a handover case both ways is one in which a laboratory like environment the driver in the loop simulation. Where you can precisely and repeatedly create exactly known ground truth, as they are referred to. You know exactly the situation in which the car and the driver system was - interacting with. You can repeat and observe precisely as well. This brings great benefits in terms of time to market but also the quality to market. Host: Okay, then. Would one of your driver in the loop simulators help us when-the manufacturer when the trying to work out what would happen driver would do something different to the assistive driving system. I mean, how do you sort out that mess? Kia: Absolutely, uh-mm. We have the case where the humans are in full control. We have the case where the vehicle systems are in full control. We consider the handover between the two. But, what happens when the human thinks that he or she is in full control and the assistive system decides to help. It can do so subtly. The human might not be aware that it is happening or it can do so in a very direct counterintuitive fashion. Perhaps it is based on a safety case. Now, if it'd assist the human in a way that is startling or unexpected how does the human then react? And the startle response will then produce a reaction from the human. The system designer will find it very difficult to predict without detailed lab condition experiments. So, you can evaluate and quantify that. Host: So, you've been doing this work already presumably. Kia: Customers of ours have been doing the work. Hopefully with a lot of the right tools for them. But, uh-mm yes. Host: I can imagine that simulators such as this would allow you to do some very good and detailed comparisons between an actual driver, driving the vehicle and the car driving itself and what happens. Kia: Yes. So, the driver in the loop system is an ideal place to do that sort of comparative study. You have the ability to re-create the scenario or one of thousands of scenarios many times as many times as you need to gain statistical confidence. You can play that scenario to a completely unassisted human driver, see how they behave and look at the outcome. It might be a good or bad outcome. It might be an accident. It might be an injury to another road user. It might be a scare. You can then bring in assistive technologies and you can model those. You can try different assistive algorithms and again look at the outcomes of the same scenarios with the statistical confidence, with absolute repeatability and absolute fidelity of measurement. Host: Right. Kia: This is an extremely powerful tool that has no real world physical analog because you just can't do this-you can't even do this sparingly in the real world. Host: So, are there any other ways in which driver in the loop systems can help develop other assistive technologies. Kia: Well, there are a few other ways. One of the interesting ones is in developing assistive algorithms. These can be determined or they can be pattern finding. If they are pattern finding, then a very good pattern to observe is that of a purely skilled human driver reacting to complex situations. You can teach the algorithms if you have the right technologies to observe the transferring function between what the human is seeing on the-assisted technology can perceive and what the human does and if it is a good outcome. You can observe that pattern and you can try to repeat that in future scenarios with purely assisted technology. Host: Well, interestingly in the notes that I was reading before we were talking here. They mentioned that this can be used by manufacturers, by the automotive companies to help develop this brand feel. I mean how do you do that. Well, in the brave new world of adaptive technologies, assisted technologies and autonomous technologies. If we're not careful and if the manufacturers are not careful will all end up driving a featureless pot nothing to differentiate one from another except for perhaps cost. That is not going to do anybody any good. So, you have some way if you are a manufacturer and you have brand values. You have to have some way of projecting those values into the future and into the new world that we are moving into. If we are talking driverless they are big challenges but it can be addressed. But, before we get to driverless they will be part driverless and driver assisted. The brand values are projected from everything from interior design to the audio spectrum of noises coming through and those can be tuned without directly affecting other characteristics such as ride. You have an even fully driverless case. A choice of driving profile. So, if the road ahead is clear you can accelerate more briskly for less briskly. You can take the corner quicker or less quickly. With adaptive suspensions you can predetermine the ride of differing conditions and you can project these brand values directly albeit subliminally into the occupants of the vehicle. There are many aspects to choose from and for more than you can possibly achieve practically with physical in the metal equipment. So, the simulation technologies with human driver in the loop-with occupants are a very good way to run through these and to test the effect on the human occupant-the perception on the human occupant of different modes of suspension, vehicle audio behavior and of vehicle system behavior. Host: I can see two massive benefits very quickly and that is they must be enormous cost savings -to be able to test in this way rather than actual cars on the road real people in them and with all the cost associated with that. Then secondly, there must be enormous health and safety benefits as well because some of the technology that is being tested potentially pose drivers cars and other people around them into dangerous situations. Whereas a simulator takes away all of that, doesn't it? Kia: Absolutely. And, not only can you test a large number of use cases in a short amount of time you can test them in a simulator with exact observation of the inputs and outputs. You can be more confident about the quality of the results. And you can also test cases that would be impossibly dangerous to do in the real world. You can have people run out in front of the vehicle and see what happens. You can have balls thrown in the air in front of vehicles and see what happens. You can have a dog come in from one side and a person from another. Even in real world works testing this is very hard to arrange, the targets that are used are relatively crude and these can be simulated better than they can-they can be simulated in the virtual world by stimulating senses with virtual models of what they would perceive in the real world. Far more accurately than you can buy building physical test equipment and you can just run through multiple drivers. One of the use cases, multiple drivers repeating exactly the same use case. You cannot practically arrange that. The time a day may change, the sun position may change and the weather may change. The vehicle itself-if you look at the physical vehicle might change but in the simulator you can arrange to test the whole suite of subjects and get statistically valid results from that. Whereas, statistically valid test results are very hard to get from physical testing because of the limitations on the number of experiments that you can practically before. And that is the case. Host: Okay, well this all sounds marvelous. Is this something that is happening today? Are the major automotive buying into this. Are you seeing good take up? Kia: I think there is they need point in the market for adoption of driver in the loop technologies. The other in the loop technologies; hard in the loop, soft in the loop, mechanical in the loop and model in the loop. They have been in common use for decades in some cases. The driver in the loop has been orphaned from these and has not played a big part in the development of the more complex car systems simply because it just was not good enough. So, the reactions that you tend to get from human subjects, even skilled human subjects were not the same as you would experience in the real world. So, the utility of putting driver in the loop with other in the loop systems was not particularly high. That is changing. The market, the OEMs and the tier one providers see now that driver in the loop is a strong technology and one that can be relied on to properly stimulate the cognitive work of the human subjects and the perceptual load of the human drives. And the cognitive work load in the perceptual loading that you can get from a good quality driver in the loop systems of still which there are still relatively few in the market. It is sufficient to inform the driver's response to such an extent that you get very high fidelity responses to unsettled interactions between human and assistant technologies. Those subtle interactions were lost before. So, to answer the questions directly. Yes, I think the technology is being taken up think recently. It is getting very wide recognition. Host: Uh-mm. Fascinating, valuable and very interesting work you are doing their Kia. Thank you very much for your explanation and hopefully one day I can come there and try one of these machines for myself. Kia: Please do, thank you there." 
}
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```