Article Impact of Autonomous Vehicles on the Physical Infrastructure: Changes and Challenges
Kareem Othman 1,2
1 Civil Engineering Department, University of Toronto, 35 St. George, Toronto, ON M5S 1A4, Canada; [email protected] or [email protected] 2 Public Works Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
Abstract: Over the last few years, autonomous vehicles (AVs) have witnessed tremendous worldwide interest. Although AVs have been extensively studied in the literature regarding their benefits, impli- cations, and public acceptance, research on the physical infrastructure requirements for autonomous vehicles is still in the infancy stage. For the road infrastructure, AVs can be very promising; however, AVs might introduce new risks and challenges. This paper investigates the impact of AVs on the physical infrastructure with the objective of revealing the infrastructure changes and challenges in the era of AVs. In AVs, the human factor, which is the major factor that influences the geometric design, will not be a concern anymore so the geometric design requirements can be relaxed. On the other hand, the decrease in the wheel wander, because of the lane-keeping system, and the increase in the lane capacity, because of the elimination of the human factor, will bring an accelerated rutting potential and will quickly deteriorate the pavement condition. Additionally, the existing structural design methods for bridges are not safe to support autonomous truck platoons. For parking lots, AVs have the potential to significantly increase the capacity of parking lots using the blocking strategy. However, the implementation of this parking strategy faces multiple issues such as the inconsistent Citation: Othman, K. Impact of marking system. Finally, AVs will need new infrastructure facilities such as safe harbor areas. Autonomous Vehicles on the Physical Infrastructure: Changes Keywords: autonomous vehicles; geometric design; pavement design; bridges; infrastructure and Challenges. Designs 2021, 5, 40. requirements https://doi.org/10.3390/ designs5030040
Academic Editors: Said M. Easa and Shriniwas Shrikant Arkatkar 1. Introduction Over the last few years, autonomous vehicles (AVs) have gained significant attention Received: 4 June 2021 from both research and industry because of the rapid innovations in artificial intelligence Accepted: 5 July 2021 technology in addition to the capabilities of the new sensing technology [1–3]. In general, Published: 8 July 2021 there are five levels of automation functionality, according to the National Highway and Transportation Safety Administration. Level 0: no automation. Level 1: automation of one Publisher’s Note: MDPI stays neutral control function such as lane keep assist or autonomous control, level 2: automation of two with regard to jurisdictional claims in control functions, level 3: limited self-driving but expect the driver to take control at any published maps and institutional affil- time with adequate warning, level 4: drivers are not expected to take control at any time iations. of the trip, level 5: full self-driving with no human control [4]. Given the rapid growth in vehicle automation technology, there is a pressing need to uncover the impact of AVs on the physical infrastructure. From the road infrastructure perspective, AVs can be very promising as it is frequently Copyright: © 2021 by the author. mentioned in multiple studies that AVs have the potential to increase traffic safety, reduce Licensee MDPI, Basel, Switzerland. vehicle ownership, allow passengers to be engaged in other activities (which means that This article is an open access article the trip time will not be considered as an economic loss), reduce the parking demand which distributed under the terms and in turn reduces congestion of vehicles searching for a parking space, reduce emissions and conditions of the Creative Commons energy due to the platooning effect, the smooth start and stops, and the reduction in the Attribution (CC BY) license (https:// number of engines starts due to the reduction in required fleet size, increase coverage and creativecommons.org/licenses/by/ accessibility for aged, disabled individuals, and people with limited transportation [5–33]. 4.0/).
Designs 2021, 5, 40. https://doi.org/10.3390/designs5030040 https://www.mdpi.com/journal/designs Designs 2021, 5, 40 2 of 30
However, AVs might introduce new risks and challenges for the current infrastructure. For instance, the change in the vehicle behavior or the driving behavior is expected to introduce new risks and deteriorate the performance of the existing network. Thus, this impact might have a significant impact on the economic and environmental costs. As mentioned above, AVs have gained significant attention from researchers in terms of their implications, benefits, technological requirements, and public acceptance [5–34]. However, how AVs will be deployed in the future mainly depends on the level of invest- ment from the public and private sectors in the infrastructure. Research on the physical infrastructure requirements for autonomous vehicles is still in the infancy stage that even the highways and roads planning and guidance documents followed by most transporta- tion authorities, such as DMRB in the UK, do not take into account AVs at all [35]. Although it is expected that AVs will change both the digital and physical infrastructure, little at- tention has been paid to the physical infrastructure requirements and the main focus of the existing studies in the literature is on the digital infrastructure [36]. Thus, the main objective of this paper is to assess the physical infrastructure requirements to support the deployment of AVs. This paper focus on analyzing the impacts of AVs on multiple branches of the physical infrastructure as follows: 1. The impact of AVs on the geometric design: in this study, the AASHTO design guidelines will be reviewed and updated to analyze the impact of deploying AVs on the stopping sight distance, passing sight distance, horizontal curve, and vertical curve design. 2. The impact of AVs on the design of the parking lots. 3. The impact of AVs on the pavement design. 4. The impact of AVs on the structural design of bridges. 5. Infrastructure requirements and new risks or challenges that will be introduced with the deployment of AVs: such as the required safe harbor areas, the need for a traffic management technique, and the required signing and marking.
2. Geometric Design This section focuses on investigating the possible change in the geometric design elements when a fully AVs fleet navigates on the roads. In general, driving behavior is the major factor that influences the design elements [37]. For example, the driver’s reaction time, eye height, and other human-related behaviors are the major factors that influence the design of highway geometric elements. The study by McDonald highlighted that the geomatic design elements will change once the entire fleet consists of AVs without detailed analysis [38]. Thus, this section focuses on the changes in the geometric elements that will be affected by switching from traditional driving to autonomous driving such as the stopping sight distance, the decision sight distance, and the length of the vertical curve. Additionally, in this section, the current AASHTO standard will be revised and updated in order to be compatible with autonomous driving.
2.1. Stopping Sight Distance (SSD) Model The stopping sight distance (SSD) consists of two main distances: the perception and reaction distance and the braking distance [39]. The perception and reaction distance mainly depends on the driver’s attention and response. On the other hand, the braking distance depends on the mechanical characteristics of the vehicle. In this study, the mechanical performance of the human-driven and autonomous vehicles will be assumed to be identical. Additionally, the surrounding conditions such as weather conditions, grade, and road surface condition will be assumed to be identical. As a result, the braking distance remains the same in the two cases. In a mathematical formula, the SSD can be expressed using Equation (1). V2 SSD = 0.278 ∗ V ∗ t + a (1) 254 9.81 ± G Designs 2021, 5, x FOR PEER REVIEW 3 of 33
and road surface condition will be assumed to be identical. As a result, the braking dis- tance remains the same in the two cases. In a mathematical formula, the SSD can be ex- pressed using Equation (1). = 0.278 ∗ ∗ + (1) 254 ( ) ± 9.81 Where: SSD = Stopping Sight Distance (m) V = Design Speed (km/h) t = Perception and reaction time (s) (Typically 2.5 s) a = Deceleration Rate (m/s2) (Typically 3.4 m/s2) G = Grade of the Road Previous studies that focus on the reaction of AVs show that AVs can react quickly, in 0.2 s, after the detection of an obstacle [40] then the vehicle starts braking. In this study, a reaction time of 0.5 s, which is a conservative value, will be used. The difference between the SSD of AVs and human-driven vehicles is shown in Figure 1. The resulting SSDs for the human-driven vehicles and AVs, resulting from the previous equation in the case of level roads, are shown in Table 1. Figure 2 shows the SSD for different upward grades for both human-driven vehicles and AVs, while Figure 3 shows the SSD values for down- grades. The figures illustrate that AVs can significantly reduce the SSD due to their quick reaction. Designs 2021, 5, 40 3 of 30 Table 1. SSD on level roads for human-driven vehicles and autonomous vehicles (AVs).
Design Human-Driven Vehicles AVs SSD Speed PRD Braking where: SSD (m) PRD Braking SSD (m) Difference (km/h) (m) Distance (m)• CalculatedSSD = Stopping Design Sight Distance(m) (m)Distance (m) Calculated Design (m) 20 13.9 4.6 • V18.5= Design Speed20 (km/h)2.78 4.6 7.38 5 15 30 20.9 10.3 • t =31.2 Perception and30 reaction4.17 time (s) (Typically10.3 2.5 s) 14.5 15 15 40 27.8 18.2 • a =46 Deceleration 45 Rate (m/s5.562) (Typically 18.2 3.4 m/s2) 23.8 25 20 50 34.8 28.5 • G63.3= Grade of the65 Road 6.95 28.5 35.5 35 30 60 41.7 41 Previous82.7 studies85 that focus8.34 on the reaction41 of AVs show49.3 that AVs50 can react quickly,35 70 48.7 55.9 in 0.2 s,105 after the detection105 of an9.73 obstacle [4055.9] then the vehicle65.6 starts braking.65 In this40 study, 80 55.6 73 a reaction129 time of 0.5130 s, which is11.1 a conservative 73 value, will84.1 be used. The difference85 between45 90 62.6 92.3 the SSD155 of AVs and human-driven155 12.5 vehicles 92.3 is shown in Figure105 1. The resulting105 SSDs50 for the 100 69.5 114 human-driven184 vehicles185 and AVs,13.9 resulting 114 from the previous128 equation130 in the case55 of level 110 76.5 138 roads, are214 shown in215 Table 1. Figure15.32 shows138 the SSD for different153 upward155 grades for60 both 120 83.4 164 human-driven248 vehicles250 and AVs,16.7 while Figure164 3 shows the181 SSD values180 for downgrades.70 The figures illustrate that AVs can significantly reduce the SSD due to their quick reaction. 130 90.4 193 283 285 18.1 193 211 210 75
Figure 1. The stopping sight distance for human-driven vehicles and AVs. Figure 1. The stopping sight distance for human-driven vehicles and AVs.
Table 1. SSD on level roads for human-driven vehicles and autonomous vehicles (AVs).
Human-Driven Vehicles AVs Design SSD Speed SSD (m) SSD (m) PRD Braking PRD Braking Difference (m) (km/h) (m) Distance (m) Calculated Design(m) Distance (m) Calculated Design 20 13.9 4.6 18.5 20 2.78 4.6 7.38 5 15 30 20.9 10.3 31.2 30 4.17 10.3 14.5 15 15 40 27.8 18.2 46 45 5.56 18.2 23.8 25 20 50 34.8 28.5 63.3 65 6.95 28.5 35.5 35 30 60 41.7 41 82.7 85 8.34 41 49.3 50 35 70 48.7 55.9 105 105 9.73 55.9 65.6 65 40 80 55.6 73 129 130 11.1 73 84.1 85 45 90 62.6 92.3 155 155 12.5 92.3 105 105 50 100 69.5 114 184 185 13.9 114 128 130 55 110 76.5 138 214 215 15.3 138 153 155 60 120 83.4 164 248 250 16.7 164 181 180 70 130 90.4 193 283 285 18.1 193 211 210 75 Designs 2021 5 Designs 2021,, 5,, x 40x FORFOR PEERPEER REVIEWREVIEW 44 of of 33 30
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FigureFigure 2. 2. SSDSSD for for different different upward upward grades grades for for both both human human-driven--driven vehicles vehicles and and AVs.
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FigureFigure 3. 3. SSDSSD for for different different downgrades downgrades for for both both human human-driven--driven vehicles vehicles and and AVs AVs...
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2.2. Decision Sight Distance (DSD) In general, the decision sight distance (DSD) is used to offer the drivers the safe distance that is required to make a complex maneuver or to deal with a complex situation. In other words, the DSD is used to allow the driver to detect the threat and react safely to take the correct action [41]. DSD involves multiple maneuvers starting from stopping, changing lanes, or decreasing the speed of the vehicle. The DSD is classified into five categories or five movements from A to E. Maneuvers A and B focuses on providing sufficient stopping distance on rural and urban roads and the DSD in this case is calculated using Equation (2). V2 DSD = 0.278 ∗ V ∗ t + 0.039∗ (2) a where: • DSD = Decision Sight Distance (m) • V = Design Speed (km/h) • t = Pre-maneuver time (s) • a = Driver Deceleration Rate (m/s2) The design guidelines state that avoidance maneuver A, stop on a rural road, needs a pre-maneuver time of 3 s, while avoidance maneuver B, stop on a rural road, needs a pre-maneuver time of 9.1 s [39]. The previous values are needed to offer the drivers (human drivers) the required distance to complete the maneuver safely. However, in the case of AVs, the reaction time is different from the human driver reaction time. Thus, the required distance should be shorter similar to the case of SSD. In other words, AVs will not need longer distances to react in complex situations and the reaction time for the DSD will similar to the reaction time of the SSD = 0.5 s. In this case, for maneuvers A and B, the DSD will be the same as the SSD in the case of level roads. Table2 and Figure4 show the difference between the DSD for maneuver A and B for human-driven vehicles and AVs. Additionally, the remaining three DSD maneuvers (C, D, and E) offer speed, path, or direction change on rural, suburban, and urban roads. However, these three cases are more complex than the first two cases and need further analysis and will be discussed and analyzed separately in detail in a future study.
Table 2. Decision sight distance for cases A and B for both human-driven vehicles and AVs.
Calculated (m) Design (m) Design Speed Human-Driven AVs Human-Driven AVs (km/h) A B A or B A B A or B 50 70.37 155.16 35.62 70 155 35 60 91.33 193.08 49.63 90 195 50 70 114.58 233.29 65.93 115 235 65 80 140.13 275.79 84.53 140 275 85 90 167.97 320.59 105.42 170 320 105 100 198.10 367.68 128.60 200 370 130 110 230.53 417.07 154.08 230 415 155 120 265.25 468.75 181.85 265 470 180 130 302.27 522.72 211.92 300 525 210 DesignsDesigns 20212021, ,55, ,x 40 FOR PEER REVIEW 66 of of 33 30
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Figure 4. Decision sight distance for the two scenarios A and B for both human-driven and autonomous vehicles. Figure 4. Decision sight distance for the two scenarios A and B for both human-driven and autonomous vehicles.
2.3.Additionally, Lateral Clearance the on remaining Horizontal three Curves DSD maneuvers (C, D, and E) offer speed, path, or directionIn horizontal change on curves, rural, thesuburban, line of sightand urban is used roads. for ensuring However, the these availability three cases of a safeare moresight complex distance than that allowsthe first drivers two cases to stop and beforeneed further hitting analysis a stopping and obstacle will be discussed [42–44]. While and analyzedthere are se multipleparately types in detail of horizontal in a future curves, study. this section focuses on circular horizontal curves that have a curve length that is higher than the SSD to show the impact of AVs 2.3on. theLateral required Clearance lateral on Horizontal clearance Curves distance or on the sightline. In this case, the horizontal sightlineIn horizontal offset (HSO curves,) can the be calculatedline of sight using is used the formula for ensurin showng the in availability Equation (3). of a safe sight distance that allows drivers to stop before hitting a stopping obstacle [42–44]. While 90S there are multiple types of horizontalHSO = curves,R 1 − costhis section focuses on circular horizontal(3) curves that have a curve length that is higher than theπ R SSD to show the impact of AVs on the required lateral clearance distance or on the sightline. In this case, the horizontal sight- where: line offset (HSO) can be calculated using the formula shown in Equation (3). • R = curve radius (m) • S = Sight distance (m) and inHSO general = R [1 this − cos value ( )] is used as the SSD in order to let (3) drivers stop before crashing obstacles. Where: It must be noted that the curve radius in the previous equation is calculated using the superelevation R = curve radius rate and (m) speed. Since it is assumed that AVs are similar to human-driven vehicles S = Sight except distance for the driving(m) and behavior, in general it canthis be value stated is thatused the as required the SSD minimum in order to curve let radiusdrivers will bestop the before same crashing in both cases: obstacles. human-driven and autonomous vehicles. Thus, the requiredIt must SSD be for noted AVs isthat much the lowercurve thanradius human-driven in the previous vehicles, equation and inis turn,calculated the required using thesafe superelevation HSO distance rate will beand lower speed. for Since AVs. Figureit is assumed5 shows that the requiredAVs are designsimilar controlsto human for- drivenachieving vehicles the safe except stopping for the sight driving distance behavior for, different it can be speedsstated that (60, the 80, required 100, and minimum 120 km/h) curveon Horizontal radius will Curves be the for same human in both driving cases: and human AVs. Figure-driven6 shows and autonomous the difference vehicles. in the Thus,required the saferequired values SSD of HSOfor AVs distances is much in thelower two than cases: human human-driven-driven vehicles vehicles, and in bluein turn and, theAVs required in red. safe HSO distance will be lower for AVs. Figure 5 shows the required design controls for achieving the safe stopping sight distance for different speeds (60, 80, 100, and
DesignsDesigns 20212021, ,55, ,x 40FOR PEER REVIEW 8 7of of 33 30
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Human driving Speed (km/h) V=120 V= 100 V = 80 V=60 V=120 V= 100 V = 80 V=60 AVs
Figure 5. Design Controls for Stopping Sight Distance on Horizontal Curves for human driving and AVs. Figure 5. Design Controls for Stopping Sight Distance on Horizontal Curves for human driving and AVs.
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Figure 6. The difference between the components used for Determining Horizontal Sight Distance. Figure 6. The difference between the components used for Determining Horizontal Sight Distance. 2.4. Length of Vertical Curve 2.4.2.4.1. Length Length of Verticalof Crest CurveCurve 2.4.1. LengthAccording of Crestto the CurveAASHTO’s standards, the length of the crest curve is calculated using one of the two formulas shown in Equations (4) and (5). According to the AASHTO’s standards, the length of the crest curve is calculated ∗ using one of the two formulas shownL = in Equations (4)S