Does Driving Performance Degrade Or Improve With Increased Automation

During my studies I took a human factors and ergonomics course and found it quite interesting to see the implementation of human factors and how it relates to engineering in general. I was fascinated to study the psychology of how people in general accept certain designs. For better or worse here's an essay which I wrote answering the question "Does Driving Performance Degrade Or Improve With Increased Automation".

How is driving performance affected by the introduction of automated systems? It appears the answer is more complex than a simple more or less. There has been increased research into the automation of the tasks involved with driving (Cottrell & Barton, 2013; Merat & Lee, 2012). Coupled with this there is increased study into how these automation’s affect the driver of the vehicle and the safety and reliability of the car in general. Adoption of these automation systems is very much dependant on the willingness of the general driving population to use them (Cottrell & Barton, 2013; Merat & Lee, 2012) and how they interact with the system (Carsten, Lai, Barnard, Jamson, & Merat, 2012; Larsson, 2012; Young & Stanton, 2002; Muhrer, Reinprecht, & Vollrath, 2012). In that sense human factors in driving automation is an important area of study. It can be argued that driving performance increases up to the point of the confines that the automated system works under, however as the system fails and control of the vehicle is handed over to the driver driving performance degrades to a point greater than manual operation.

What is automation and how is it implemented? Driving and operating a vehicle is a complex task which involves many physical and cognitive tasks, often times concurrently (Carsten et al., 2012). Automation systems aim to control a selection of these tasks, or even sometimes all of these tasks, thereby relieving the driver of some cognitive load and increasing the driving performance of the vehicle. The primary tasks of driving can be broken down into longitudinal and latitudinal patterns (Carsten et al., 2012), which basically mean the throttling and steering respectively. As such there has been an effort to cut away some of these tasks and subtasks by using automated systems. These automated systems often look to take care of mundane or repetitive tasks which would allow the driver to concentrate on the higher level tasks associated with operating a vehicle (Larsson, 2012).

A lower level automation system is the automation of a single task which can be coupled with warning systems. Such an example is the forward collision warning (FCW) and forward collision warning plus braking system (FCW+) (Muhrer et al., 2012). This system indicates to a driver that he/she should take action to avoid a forward collision, the plus braking component initiates the braking for the driver in the event of an imminent collision. This system also gives both visual and audio cues in an increasingly attention grabbing sequence to indicate the threat of collision. Additionally it can also be turned off by the driver allowing full manual control.

Some higher level examples include the, already commercially available Active Cruise Control (ACC) and Active Braking (AB), automated lane keeping and automated highway driving (Larsson, 2012; Muhrer et al., 2012). These automation systems operate in different ways and have different visual and auditory cues depending on the task. In the case of ACC it is engaged by the driver of the vehicle and has a visual cue on the dashboard to indicate that the system is running. The system works in tandem with the driver rather than taking over an entire task. To deactivate there are several ways, the driver can simply hit the brakes, or turn it off at the control point or the system will disengage itself when outside its boundaries of use (Larsson, 2012).

In some circumstances however there has been a focused push on automating the entire vehicle and taking the control away from the driver to the point of simply being an observer rather than an active participant in the operation of the vehicle, the so called ”driverless car” (Merat & Lee, 2012). In this circumstance the driver does not have any role and the system makes all the decisions.

Why does automation matter and what are the advantages? Automation from a high level is seen as an improvement to the current limitations of manual operation of a vehicle. The advantages include increased sensitivity to road and traffic conditions (Muhrer et al., 2012), more efficiency and better assistance and comfort to drivers, especially with low driving skills (Merat & Lee, 2012). Automation is also proposed as a solution to reducing congestion by increasing road capacity (Merat & Lee, 2012). In addition to these advantages automation systems allow the abstraction away of mundane or low level tasks allowing access to vehicle driving to a wider population. Ultimately the entire automation of the vehicle could possibly allow huge advantages to many people. These can include the accessibility to driving a large population that previously couldn’t. This includes the disabled, such as blind or physically incapable of driving, will have a level of mobility not previously accessible to them. For all these reasons automation is a sought after technology by car manufacturers and as such can require intense human factors study.

Some more specific advantages can be seem in human factors studies. Within the limitations of the automation systems they are extremely effective and reduce risk on the road (Muhrer et al., 2012). Automated systems have distinct advantages to human drivers. Some include the inability of automated systems to get distracted, automated systems use sensors which is outside the scope of human senses and automated systems sense multidirectionally. Automated systems also are able to carry out several tasks at once which humans struggle at. In addition to this the reaction time of automated systems are well above that of the average driver (Muhrer et al., 2012). Additionally automated systems do not suffer from fatigue, meaning they stay much more resilient than regular drivers.

What are the potential drawbacks of these automated systems? As automation increases there come some drawbacks which can affect the adoption and safety of these systems. One drawback is the edge cases and limitations of the system for which it was not designed for. A good example of this is for automatic throttling (ACC) the systems forward radar loses contact with the forward car due to a sharp bend in the road (Larsson, 2012). As the system starts to reach its operating limits the system has to make a decision as to when to fail and also how to fail and hand back control to the driver (Merat & Lee, 2012). At the system limitations the system has to be intelligent enough to know when and how to do this otherwise it remains a bad system. Additionally automated systems suffer from another seemingly widespread drawback, that is that with increased automation the awareness of the driver drops and increases the reaction time, and therefore risk, in situations where the system fails and the driver has to take over operation of the vehicle (Cottrell & Barton, 2013; Merat & Lee, 2012; Young & Stanton, 2002). Although countering the last point, if the driver is given more time and education with the system to learn its limitations and quirks, the reaction times and acceptance increases reducing the risk in the changeover of control (Larsson, 2012). This can be demonstrated by (Larsson, 2012) that as time and experience with the ACC increases the driver is more aware of the limitations and the situations in which ACC fails, such as bends and roundabouts, and therefore the awareness in these situations increases.

There is also another argument that many of these automated systems can be disparate and piecemeal which can hinder education and acceptance of these automated systems (Merat & Lee, 2012).

Another interesting drawback is if the system is only a warning system than the drivers reaction time may decrease even with the warnings in place (Muhrer et al., 2012). This is particularly interesting as it indicates a seemingly unique issue of under automation. The system does not take over enough from the driver and hence puts the driver at a larger risk.

On top of these drawbacks there is also the a specific human factor drawback of acceptance of the automation systems. As automation systems are introduced to a vehicle it necessitates an adoption by the driver. Acceptance and trust must be given by the driver to the system. This can prove difficult. Often the driver must learn new and different behaviours which creates a barrier to entry. Often times drivers can be slow to learn these new behaviours, sometimes to the point where drivers may give up and not use them (Larsson, 2012). Conversely when the driver learns the behaviours and limitations of an automation system then the acceptance and trust raises (Larsson, 2012). In this particular study as experience with the system increased so did the acceptance of the system.

How can we interpret the results of human factors studies and determine how driving performance is affected? So we have automated systems that create a safer and better driving experience within the limitations of the system. But when the limitations are breached there remains a larger risk and loss of driver awareness and reaction time due to the inattention these systems allow (Cottrell & Barton, 2013; Merat & Lee, 2012). We also have a gap of education due to unfamiliarity with the system of the driver and the drivers knowledge of its limitations. However as this education gap is overcome the risk and awareness gap shrink as well (Larsson, 2012). It appears a gap of education is a barrier to entry for the adoption of these vehicle systems and we therefore have a vicious cycle of adoption and awareness of the system limitations. To keep driving performance at a desirable level there seem to be a few strategies in relation to these automated systems. The simplest strategy is to simply not automate the driving of a vehicle at all and keep all the tasks the responsibility of the driver (Muhrer et al., 2012). There are some advantages to this, in that the driver is always in control and always comfortable with the control he/she has of the vehicle. This allows for quicker awareness in emergency situations. Countering this though the more mundane tasks of regular driving can reduce the awareness of the driver to the tasks and subtasks needed for regular operation (Young & Stanton, 2002). This results in higher stress overall but within the confines of an acceptable and sometimes optimal level of stress for the operation of a vehicle (Cottrell & Barton, 2013).

To the ultimate conclusion of full automation it seems we eliminate this gap of risk and inattention. If the fully automated car is capable and indeed possibly better than a driver at all the tasks needed for operating a vehicle than the elimination of the driver and thus the gap of inattention and risk driving performance should be completely improved. The only drawback to the driverless car is that acceptance is very low as drivers prefer to have the illusion of control (Merat & Lee, 2012). But to counter this drivers enjoy a more integrated approach to rather than piecemeal implementations of various disparate automation systems (Merat & Lee, 2012), therefore if the driverless car has an appealing automated experience than adoption may well remain high despite the removal of driver control.

There are however some points to make about the research and testing currently available. Many of the studies are done within the confines of a simulated environment (Young & Stanton, 2002; Muhrer et al., 2012; Carsten et al., 2012) which is a limitation of the studies. One which possibly must be put up with due to health and safety concerns of undertaking these tests on the open road. It is possible that participants in these tests behave differently in a simulator as opposed to how they act on the open road. One study was able to research drivers in the real world (Larsson, 2012) via a survey. However the sample size remains small with all the respondents electing to pay extra to have the automated system installed, thereby possibly introducing some confirmation bias into the study results. The final two papers (Cottrell & Barton, 2013; Merat & Lee, 2012) being meta analyses of various papers the conclusions drawn can be trustworthy provided the papers these analyses have drawn from are reliable. So it is clear that more than just these papers must be taken into account for a more rigorous understanding of drivers reaction to automation and how this affects performance on the road.

The overall takeaway from these studies can be as follows. Under the management of an automated system driving performance improves. Sometimes it even improves significantly. However when the system fails or returns control to the driver there is a period of time where driving performance degrades to a point lower than with manual operation. After this period performance is restored. There seems to an antidote to this problem, with increased education and familiarity this gulf of performance can be overcome. The challenge is to implement more standardised systems which cultivate familiarity and keep the driver educated about these systems and their quirks and limitations.

In conclusion using automation systems to take over driving tasks has many advantages and car manufacturers seem to be moving in the direction of offering these systems to drivers more and more. However as driving becomes more and more automated there tends to be a moving gap of acceptance and safety as drivers have to learn to operate and understand these systems and limitations. As such driving does improve with automation within the confines of the systems limitations. Outside of these limitations driving performance can degrade for a critical amount of time, but with education and awareness of system limitations this gap may be minimised. As such we tend to trend towards fully automated systems where the system is inherently more capable than the driver and hence driving performance improves with automation, but with a moving gap of driver-system interface.


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