Safety

According to recent studies, CAVs have the potential to dramatically improve road safety. The safety benefits include mitigating crash severity and decreasing the possibility of crashes, (Gurney, 2014; Poczter and Jankovic, 2014; Rodoulis, 2014) by eliminating the human factor which is the primary cause of crashes in 94% of the cases according to NHTSA (Singh, 2015). Table 2.2 summarises potential causes of 5.5 million crashes in the U.S. It is also noticeable that some combinations of factors (i.e. alcohol, driver distraction, drugs and fatigue which are shown in bold characters in the table) are responsible for causing a 40% of the fatal crashes (Fagnant & Kockelman 2015). In their paper, the authors speculated that autonomous vehicles would reduce fatal crashes by at least 40% as they would not be susceptible to human failings. This reduction however does not reflect the crashes caused by speeding, aggressive driving, inexperience, slow reaction time, inattention and other human driver faults.

Table 2.2 U.S. crash motor vehicle scope and selected human and environmental factor involvement source: (Fagnant & Kockelman 2015) (edited)

Total crashes per year in U.S. (National Highway Traffic Safety Administration, 2013b)

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Total fatal and injurious crashes per year in U.S. (National Highway Traffic Safety Administration, 2013b)

2.22 million

Fatal crashes per year in U.S. (National Highway Traffic Safety Administration, 2012)

32,367

% human cause as primary factor (National Highway Traffic Safety Administration, 2008)

93%

% of fatal crashes involving alcohol (National Highway Traffic Safety Administration, 2012)

31%

% involving speeding (National Highway Traffic Safety Administration, 2012)

30%

% involving distracted driver (National Highway Traffic Safety Administration, 2012)

21%

% involving failure to keep in proper lane (National Highway Traffic Safety Administration, 2012)

14%

% involving failure to yield right-of-way (National Highway Traffic Safety Administration, 2012)

11%

% involving wet road surface (National Highway Traffic Safety Administration, 2012)

11%

% involving erratic vehicle operation (National Highway Traffic Safety Administration, 2012)

9%

% involving inexperience or overcorrecting (National Highway Traffic Safety Administration, 2012)

8%

% involving drugs (National Highway Traffic Safety Administration, 2012)

7%

% involving ice, snow, debris, or other slippery surface (National Highway Traffic Safety Administration, 2012)

3.70%

% involving fatigued or sleeping driver (National Highway Traffic Safety Administration, 2012)

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% involving other prohibited driver errors (e.g. improper following, driving on shoulder, wrong side of road, improper turn, improper passing etc) (National Highway Traffic Safety Administration, 2012)

21%

In the same study, it is estimated that the safety benefit of autonomous vehicles would depend on the market penetration rate, predicting a 50% reduction in crash rate and a 50% reduction in injury rate at the 10% CAV market penetration rate. The authors additionally predict 90% reduced crash and injury rates at the 90% market penetration rate. By using Insurance Institute for Highway Safety’s accident data, a similar estimation is achieved by Anderson et al. (2014) who predict a 39% reduction in crash fatalities during the fully automated era. Unfortunately, the results of the above studies cannot be compared directly as different performance indicators are used (crash/injury rate vs number of crash fatalities).

Without providing exact predictions on the safety impact of autonomous vehicles, Ni & Leung (2014) speculate an analogy of automated aviation technologies with CAVs. They imply that since automated aviation technologies reduced aviation accidents, autonomous vehicles will possibly improve road safety. Finally, Kim et al. (2015) state that, ideally, when the penetration rate of autonomous vehicles reaches 100% accidents could be reduced to zero.

More recently, studies have focused on traffic simulation in order to estimate the safety impact of CAVs. Motorways are arguably the simplest road network layout, (no interaction with other road users such as pedestrians etc) and this is the reason why most studies focus their efforts on this type of network (Kockelman and Hanna, 2016; Jeong, Oh and Lee, 2017). Both studies used rear end and lane changing traffic conflicts as an indicator of safety. Traffic conflict is defined as an event involving two or more moving vehicles approaching each other in a traffic flow situation in such a way that a traffic collision would ensue unless at least one of the parties performs and evasive manoeuvre (Parker and Zegeer, 1988). The results of these studies are not necessarily comparable due to differences in the underlying CAV control algorithms, network parameters (number of lanes, number of merging and diverging areas) but for good measure they are summarised in Figure 2.2. The results for Chen et al’s study

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seemed to be similar to the observations of Jeong et al. for medium to high traffic flow volumes. However, a big change is observed in low traffic flow value’s in Chen’s study.

Figure 2.2 Traffic conflicts change % on motorways per market penetration rate (0%, 25%, 50%, 75%, 100%)

Furthermore, motorway related studies have used several other surrogate safety measures to evaluate safety such as Time to Collision (TTC) and its derivatives, Time Exposed Time-to-Collision and Time Integrated Time-to-collision (Jeong, Oh and Lee, 2017; Li et al., 2017a; Rahman and Abdel-Aty, 2018). The results of these studies once again are based on the assumptions and the configurations of their experiments and are not comparable. Hence, listing the actual results would be in vain without a reference value. However, in summary, all the above studies proved that CAVs will bring a great safety benefit to a motorway network.

Despite the encouraging results, new, unprecedented concerns arise from the implementation of CAVs. Numerous studies express fears about hardware and software reliability and the protection against potential malicious attacks from hackers. The ability of a CAV to recognise quickly and reliably objects in the roadway is often challenged in the literature (Dalal and Triggs, 2005; Farhadi et al., 2009; The

0% 25% 50% 75% 100%

Jeong et al 0.00% -26.00% -45.00% -75.00% -95.00% Kockelman - low traffic 0.00% 80.00% 40.00% -20.00% -40.00% Kockelman-medium traffic 0.00% -16.06% -37.96% -63.50% -87.59% Kockelman-high traffic 0.00% -11.71% -29.36% -46.04% -65.31% -120.00% -100.00% -80.00% -60.00% -40.00% -20.00% 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% % C h an g e o f tr af fic co n flicts

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Economist, 2012). The difficulty does not rely on the task of recognising an object solely, but on the extremely small margin for error (Fridman, Jenik and Reimer, 2016). An object in a roadway may be small or large, with various heights, a human on the road can be walking, sitting or lying down resulting in a partially obscured view. Vasic & Billard (2013) identify faulty electronic parts and loose connections across parts as a reason leading to accidents during human-robot interaction. Furthermore, weather conditions of snow, fog and reflective road surfaces from rain and ice or even a bright sky can create challenges for sensors and driving operations.

An unfortunate example of a sensor failure is the crash occurred with a Tesla’s automated vehicle in July, 2016 (The Guardian, 2016). In this particular crash, the car’s sensors failed to detect a large white 18-wheel truck and trailer crossing the highway, against a bright sky, which resulted in the death of the driver of the automated vehicle. Figure 2.3 strongly supports these concerns of CAV safety as a percentage of disengagements from autonomous driving mode according to the Waymo’s annual disengagement report happened due to strangely looking objects or aggressively moving vehicles.

Thankfully, despite the aforementioned concerns, results so far seem encouraging as Fridman et al. (2016) state that CAVs would be able to predict their failures as technologies reach higher readiness levels. For instance, as far as disengagements are concerned, there is a noticeable decline according to the Waymo’s annual disengagement report (Waymo, 2016). Specifically, the disengagements per thousand miles in 2016 were reduced to 0.2 compared to 0.8 in 2015.

Furthermore, a plethora of papers raise awareness about what would happen if bugs would occur in CAV software (Pinto, 2012; Vasic and Billard, 2013; Ni and Leung, 2014; Schellekens, 2015; Kelly et al., 2016). For instance, Schellekens (2015) brings up the example of a fatal accident due to a software bug in a non-Automated Toyota vehicle. Vasic & Billard (2013) seem to agree as they identify programming bugs and faulty algorithms as causal factors behind accidents in Human-Robot interactions. Furthermore, Pinto (2012), Ni & Leung (2014) and Kelly et al. (2016) state bugs as a potential barrier to CAV implementation. In fact, this concern is proven by the annual disengagement report of Waymo (Waymo, 2016). As Figure 2.3 shows, a significant

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percentage of the disengagements from autonomous driving happened due to a software discrepancy.

Cause of disengagement 2015 2016 2017 2018

1 Weather conditions 0 189 189 55

2 Road surface conditions 0 0 0 21

3 Software discrepancy 0 51 51 734 4 Hardware discrepancy 0 0 0 171 5 Reckless user 20 10 10 78 6 Control discrepancy 0 170 170 35407 7 Perception discrepancy 95 132 132 402 8 Manual takeover 0 0 0 54888 9 Testing 687 1443 1443 185 10 Localisation discrepancy 0 4 4 555 11 System fault 8 2 2 23 12 Geometric configuration 0 0 0 4

13 Motion trajectory planning 0 96 96 1047

14 Communication 0 0 0 2380

15 Indeterminable 0 0 0 58

16 Construction zone 17 2 2 1

17 Debris in roadway 0 2 2 0

18 Lane detection 111 0 0 5

Figure 2.3 Cause of disengagements from Autonomous driving mode.

Furthermore, knowing where the autonomous mode failures happen would be valuable to vehicle manufacturers and researchers. The majority of the disengagements happen in complex urban environments “streets” as mentioned in Figure 2.4.

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Figure 2.4 Location of disengagements from Autonomous driving mode

Finally, studies express concerns about cyber security (Ni & Leung 2014.; Petit & Shladover 2014; Litman 2015). More specifically, Fagnant & Kockelman (2015) mention that transport policy makers, automotive manufacturers and future CAV drivers often worry about electronic security. Computer hackers, disgruntled employees and terrorist organizations could be potential threat to the implementation of CAVs, as they could use CAVs to create traffic disruptions and cause collisions. In the worst case scenario according to Fagnant & Kockelman (2015), someone could develop a two-stage virus in which they could control an entire fleet of vehicles for two weeks and then use them in order to cause a massive nation-wide catastrophe. In their conclusions, the authors are however being optimistic, stating that U.S. have managed to maintain a secure, large, critical, national infrastructure system including power grids and air traffic control systems which could possibly extend to CAVs. In addition, Pinto (2012) acknowledges the hacking problem in CAVs and describes the solution as keeping the CAV as isolated as possible from outside inputs.

Interstate 13% Freeway 1% Highway 34% Urban Street 52% Parking Facilities 0%

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Figure 2.5 Mean response on a scale from 1 to 5 regarding respondents worries about Fully Automated Driving (Kyriakidis, Happee and Winter, 2015)

A study conducted by Kyriakidis et al. (2015) investigating user acceptance, concerns and willingness to buy CAVs, seems to agree with the previous paragraph, highlighting cyber security and misuse of CAVs as the first public concern. As it can be seen in Figure 2.5, most of the respondents agreed that misuse and safety are in the top 3 concerns regarding CAVs, a finding that is also in line with Schoettle & Sivak (2014). As perfect as autonomous vehicles can be, accidents may never be eliminated completely, even during the fully automated era because simply “systems fail” as it is argued by Smith (2012). Additionally, safety problems, not known yet to CAV developers, may emerge in the following years.