Motorists' Preferences for Different Levels of Vehicle Automation: 2016

[FrodosPet]

http://www.umich.edu/~umtriswt/PDF/SWT-2016-8_Abstract_English.pdf

Brandon Schoettle and Michael Sivak - May 2016

9. Performing Organization Name and Address
The University of Michigan
Sustainable Worldwide Transportation
2901 Baxter Road
Ann Arbor, Michigan 48109-2150 U.S.A.
http://www.umich.edu/~umtriswt/

Abstract

This report builds on our recent series of eight reports addressing public opinion, human factors, and safety-related issues concerning self-driving vehicles. An identical survey to that used in 2015 was administered. The survey was developed to examine motorists’ preferences among levels of vehicle automation, including preferences for interacting with and overall concern about riding in self-driving vehicles. The survey yielded completed responses from 618 licensed drivers in the U.S.

The main findings are as follows:

• The most frequent preference for vehicle automation continues to be for no self-driving capability, followed by partially self-driving vehicles, with completely self-driving vehicles being the least preferred choice.

• Concern for riding in self-driving vehicles remains higher for completely self-driving vehicles than for partially self-driving vehicles.

• Respondents still overwhelmingly want to be able to manually control completely self-driving vehicles when desired.

• Preferences were generally divided between touchscreens or voice commands to input route or destination information for completely self-driving vehicles.

• Most respondents prefer to be notified of the need to take control of a partially self-driving vehicle with a combination of sound, vibration, and visual warnings.

• Overall public opinion has been remarkably consistent over the two years that this survey has been conducted. The general patterns of responses have not changed over the course of these two surveys, despite the increased media coverage of self-driving vehicles.

[FrodosPet]

1. The July 2015 Full Report

MOTORISTS' PREFERENCES FOR DIFFERENT LEVELS OF VEHICLE AUTOMATION

https://deepblue.lib.umich.edu/bitstream/handle/2027.42/114386/103217.pdf?sequence=1&isAllowed=y

BRANDON SCHOETTLE
MICHAEL SIVAK

University of Michigan
Transportation Research Institute
Ann Arbor, Michigan 48109-2150

~ snip ~

Preferred level of vehicle automation

When respondents were asked about which level of vehicle automation they
preferred (see the appendix for the definitions of each level of automation that were provided to respondents), the most frequent preference was for no self-driving (43.8%), followed by partially self-driving (40.6%), with completely self-driving being the least preferred (15.6%). Figure 1 summarizes the results for all respondents, while Table 2 presents a complete summary of responses by gender and age.

Females most frequently preferred no self-driving (47.6%), while males preferred partially self-driving (41.2%).

~ snip ~

Concern about riding in self-driving vehicles

In two different questions, respondents were asked how concerned they would be about riding in a completely self-driving vehicle (Q2) and a partially self-driving vehicle (Q5). The respondents were more concerned about riding in a completely self-driving vehicle than in a partially self-driving vehicle. For example 35.6% were very concerned about riding in a completely self-driving vehicle (and 68.3% were very or moderately concerned), as opposed to 14.1% for a partially self-driving vehicle (with 48.8% being very or moderately concerned). Conversely, 10.6% were not at all concerned with riding in a completely self-driving vehicle, as opposed to 16.2% for a partially self-driving vehicle. Figure 2 summarizes the results for all respondents, while Tables 3 and 4 present complete summaries of responses by gender and age.

Females expressed greater concern than males for riding in completely self-driving vehicles (very concerned: 40.1% versus 30.7%), but the difference was smaller for partially self-driving vehicles (very concerned: 15.7% versus 12.2%).

~ snip ~

[FrodosPet]

2. Road Safety with Self-Driving Vehicles - January 2015

https://deepblue.lib.umich.edu/bitstream/handle/2027.42/111735/103187.pdf?sequen

ROAD SAFETY WITH SELF-DRIVING VEHICLES:
GENERAL LIMITATIONS AND ROAD SHARING
WITH CONVENTIONAL VEHICLES

Michael Sivak
Brandon Schoettle

The University of Michigan
Transportation Research Institute
Ann Arbor, Michigan 48109-2150
http://www.umich.edu/~umtriswt

Abstract

Self-driving vehicles are expected to improve road safety, improve the mobility of those who currently cannot use conventional vehicles, and reduce emissions. In this white paper we discuss issues related to road safety with self-driving vehicles. Safety is addressed from the following four perspectives: (1) Can self-driving vehicles compensate for contributions to crash causation by other traffic participants, as well as vehicular, roadway, and environmentalfactors? (2) Can all relevant inputs for computational decisions be supplied to a self-driving vehicle? (3) Can computational speed, constant vigilance, and lack of distractibility of self-driving vehicles make predictive knowledge of an experienced driver irrelevant? (4) How would road safety be influenced during the expected long transition period during which conventional and self-driving vehicles would need to interact on the road?

The presented arguments support the following conclusions: (1) The expectation of zero fatalities with self-driving vehicles is not realistic. (2) It is not a foregone conclusion that a self-driving vehicle would ever perform more safely than an experienced, middle-aged driver. (3) During the transition period when conventional and self-driving vehicles would share the road, safety might actually worsen, at least for the conventional vehicles.

~ snip ~

3. Limitations on road safety with self-driving vehicles

Road safety with self-driving vehicles will be considered from four perspectives:

● Can self-driving vehicles compensate for contributions to crash causation by
other traffic participants, as well as vehicular, roadway, and environmental factors?

● Can all relevant inputs for computational decisions be supplied to a self-driving
vehicle?

● Can computational speed, constant vigilance, and lack of distractability of self-driving vehicles make predictive knowledge of an experienced driver irrelevant?

● How would road safety be influenced during the expected long transition period during which conventional and self-driving vehicles would need to interact on the road?

3.1 Contributions of other traffic participants, as well as vehicular, roadway, and environmental factors to crash causation

Not all crashes are caused by drivers. Some crashes are the consequence of
inappropriate actions by other traffic participants (e.g., jaywalking pedestrians), vehicular defects (e.g., failed brakes), roadway factors (a large pothole leading to a loss of vehicle control), or environmental factors (e.g., localized, sudden, dense fog). For example, Lee and Abdel-Aty (2005) found pedestrians to be at fault in 80% of pedestrian crashes at intersections. Could self-driving vehicles compensate for all non-driver factors?

3.1.1. Other traffic participants. Self-driving vehicles could compensate for some but not all crashes caused by other traffic participants. As an example of the latter, consider a situation involving a drunk pedestrian stepping suddenly into the roadway. If the distance to the pedestrian is very short, the limiting factor might not be human reaction time but the stopping distance of the vehicle (i.e., the efficiency of the brakes). Thus, although a self-driving vehicle could, in principle, respond faster than a human driver and provide optimal braking performance, it still might not be able to stop in time because of braking limitations.

Another set of challenges involving other traffic participants requires recognizing
and negotiating unusual road users. Examples include ridden horses and horse drawn buggies, large non-automotive farm equipment, and situations where police or construction crews are required to direct traffic.

3.1.2. Vehicular factors. A small (but non-zero) percentage of crashes are the consequence of vehicular failures. (Approximately 1% of fatal crashes in 2013 involved a vehicular equipment failure as a critical pre-crash event [U.S. Department of Transportation, 2015].) On one hand, some current vehicular failures might become obsolete for self-driving vehicles. For example, lighting failures might turn out to be irrelevant to safety from the perspective of being able to control one’s vehicle at night, because self-driving vehicles might not rely on visual input. (However, such failures would not be irrelevant from the perspective of other road users being able to see the vehicle in question.) On the other hand, there is no reason to expect that certain other vehicular failures (e.g., brakes or tires) would be less frequent on self-driving vehicles than on conventional vehicles. Indeed, given the complexity of the sensing hardware and of the information-processing software, it is reasonable to expect that, overall, vehicular factors would likely occur more frequently on self-driving vehicles than on conventional vehicles.

3.1.3. Roadway factors. It is expected that self-driving vehicles will eventually be able to cope with most roadway factors. Examples include large potholes and large roadway debris. However, certain other conditions (e.g., a flooded roadway or a downed power line) are likely to provide difficulties to self-driving vehicles for years to come.

3.1.4. Environmental factors. The current prototypes of self-driving vehicles cannot yet operate safely in fog, snow, or heavy rain (e.g., Lavrinc, 2014). This is the case because, under such conditions, the current sensing technology cannot provide sufficient information for reliable travel. Even if solutions are eventually found for steady-state conditions, a sudden onset of such inclement weather might not be detected in time to adjust the vehicle speed sufficiently.

~ snip ~