The Social Acceptance of Automated Driving Systems: Safety Aspects

Abstract

Automated driving systems (ADS) can improve traffic safety, improve accessibility and reduce environmental impact (Shladover, 2016). On the contrary, on May 7th 2016, a fatal accident with a Tesla on autopilot in U.S. Florida was a harsh reminder that the technology is still in its testing phase (Greenemeier, 2016). In complex technical systems like ADS technical failure may occur, which forms a serious threat to human well-being. Moreover, studies have shown that citizens are very concerned about deliberate misuse of ADS (Kyriakidis, Happee, & De Winter, 2015), e.g. people purposely abusing ADS to cause damage or even hurt someone. Therefore, according to many experts, the implementation of ADS does not only entail technical issues but also normative issues.
To bridge the gap between technical- and normative issues, responsible innovation can be applied (Santoni de Sio, 2016). Friedman et. al. (2006) propose value-sensitive design to achieve responsible innovation via technical-, empirical- and conceptual research. As following from responsible innovation and value-sensitive design and in particular its focus on empirical research as one of its necessary elements, in this research an effort is made to use empirical research methods to provide more insights in normative issues of automated driving systems (ADS). The focus lies on social acceptance, particularly with respect to traffic safety. Also accessibility, environmental impact and heterogeneity among citizens will be analysed. Social acceptance is defined as “a person's assent to the reality of a situation, recognizing a process or condition (often a negative or uncomfortable situation) without attempting to change it, protest, or exit” (Fish, 2014, page 1). The following questions will be answered in this research:
What is the social acceptance of automated driving systems from the perspective of safety, accessibility and environmental impact and what is the corresponding heterogeneity?
1. What percentage of citizens thinks automated driving systems are socially accepted?
2. How is the social acceptance influenced by safety, accessibility and environmental impact?
3. Are traffic fatalities caused by automated vehicles valued differently than current traffic fatalities?
4. Is there heterogeneity in the social acceptance among citizens?
A survey is chosen as research method since it is a relatively inexpensive, flexible method to achieve extensive information about characteristics of a population. After an extensive theoretical analysis, seven attributes were identified that possibly influence the social acceptance: level of automation, road exemption, travel time, emissions, human error fatalities, technical failure fatalities and deliberate misuse fatalities. After the experiment was fine-tuned by a pilot study, it was held among a representative sample of 510 Dutch adults during the spring of 2017. The respondents had to state if they were in favour or against ADS for each of the twelve hypothetical futures that were presented to them. In these hypothetical futures, the attributes were systematically varied and described as a change to the current situation.
Using a MNL RUM model, the results show that 63% of all citizens prefer ADS over the current system. It is therefore concluded that citizens have a high social acceptance and thus are rather positive towards ADS. Also, citizens prefer a system where human drivers are still in control and can intervene in case necessary.
Next, it is concluded that the social acceptance is mostly influenced by fatalities caused by automated vehicles (AVs), while travel time is the least important attribute. However, the differences in influence of the attributes were not substantial. Safety, accessibility and environmental impact are all important for the social acceptance. Nevertheless, technical failure fatalities weigh as much as 4 human error fatalities. For deliberate misuse fatalities this is a factor 5.5. Although these relations coincide with literature, the magnitude is larger than expected. It implies that ADS have to be very safe in order to reach social acceptance. Since AVs are still ‘learning’ how to drive, this might cause problems for current and future experiments.
A latent class choice model is estimated to answer the final research question. Results show that large heterogeneity exists among citizens in the social acceptance. Citizens can be segmented into three classes (% of citizens): automated driving enthusiasts (32%), central mass (52%) and risk-averse class (16%). Contradictory to average citizens, automated driving enthusiasts prefer high automation levels. Even so, they still weigh fatalities caused by AVs as much as 3 human error fatalities on average. The central mass shows similar results to the results of the MNL model estimated on the full sample. The risk-averse class has a strong dislike for fatalities caused by AVs. This class weighs technical failure fatalities (deliberate misuse fatalities) as much as 5.5 (10) human error fatalities.
In conclusion, primarily two discrepancies are identified that are critical for the implementation of ADS: 1) High social acceptance versus strong dislike for fatalities caused by AVs; 2) Citizens who are enthusiastic about ADS versus citizens who are risk-averse. They lead to the following recommendations:
The social acceptance for ADS is high, so it is recommended for policy makers to have a positive and active approach towards ADS. By conducting experiments for professional users, safety risks can be minimalized while a learning curve is ensured. Also technology producers and policy makers should intensify research into cooperate driving. According to experts, ADS and cooperate driving are inseparable (Shladover, 2016), but globally the research into cooperate driving is lacking (Roland Berger, 2017). Since cooperate driving can lead to an increased risk of deliberate misuse, it is deemed critical for the implementation of ADS. Next, policy makers and especially the RDW should review the licensing of AVs. Currently, hardly any restrictions are in place for the licensing of AVs, which can cause dangerous situations on public roads. Finally, information campaigns can help to make citizens aware of the risks and benefits of ADS.