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Purpose – To support the standardized evaluation of bicyclist automatic emergency braking (AEB) systems, test scenarios, test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis. This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems. Design/methodology/approach – The harmonized general estimates system (GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data (NDD) are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions, vehicle speeds, bicyclist speeds, etc. A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA. A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems. Findings – Based on the analysis of the harmonized GES/FARS crash data, ﬁve crash scenarios are recommended for performance testing of bicyclist AEB systems. Combined with TASI 110-car naturalistic driving data, the crash environmental factors including lighting conditions, obscuring objects, vehicle speed and bicyclist speed are determined. The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA. The height of the bicycle rider mannequin is 173 cm, representing the weighted height of 50th percentile US male and female adults. The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame, respectively. Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems. Originality/value – The results have demonstrated that the developed scenarios, test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems. This is crucial for the development of advanced driver assistance systems. Keywords Bicyclist, Surrogate bicyclist, Automatic emergency braking (AEB), Crash scenarios, Crash testing, Naturalistic driving, Radar cross section (RCS), Micro-Doppler Paper type Research paper 1. Introduction Bicyclist safety has attracted increasing attention by the public, © Qiang Yi, Stanley Chien, Lingxi Li, Wensen Niu, Yaobin Chen, David government agencies and transportation and automotive Good, Chi-Chih Chen and Rini Sherony. Published in Journal of Intelligent industry as a public safety and health issue. According to the and Connected Vehicles. Published by Emerald Publishing Limited. This article crash data of US Fatality Analysis Reporting System (FARS), is published under the Creative Commons Attribution (CC BY 4.0) licence. 818 bicyclists were killed, and 45,000 bicyclists were injured in Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/2399-9802.htm The authors would like to thank Toyota’s Collaborative Safety Research Center for the ﬁnancial and technical support of this research. Received 27 February 2018 Journal of Intelligent and Connected Vehicles Revised 12 April 2018 1/1 (2018) 15–27 23 April 2018 Emerald Publishing Limited [ISSN 2399-9802] [DOI 10.1108/JICV-02-2018-0005] Accepted 24 April 2018 15 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 motor vehicle trafﬁc-related crashes in 2015. The number of This paper presents the development of test scenarios, bicyclist fatalities in 2015 was about 12.2 per cent higher than surrogate bicyclist and associated hardware and software tools that in 2014 (NHTSA, 2018). Figure 1 shows the trends of the for the evaluation of bicyclist AEB systems. Accidentology is bicyclist fatalities and the percentage of bicyclist fatalities in all used to determine the primary crash scenarios. The scenario trafﬁc fatalities since 1994. It is easy to see that the number of variables are selected based on the national crash databases and bicyclist fatalities has been increasing since 2010. A total the TASI 110-car naturalistic driving data (NDD). To conduct number of 840 bicyclists were killed in crashes in 2016 the AEB performance testing, the surrogate bicyclist was (NHTSA, 2016), which is the highest number of bicyclist designed and developed, which includes a bicycle rider and a fatalities since 1991. This increase might be because of increase bicycle. Its design matches the visual and radar characteristics in the number of bicyclists between 2012 and 2017, from of the real bicyclists in the USA. The surrogate bicyclist is around 51 million to slightly more than 66 million (Statista, designed with both pedaling motion and wheel rotation to 2018). support the camera and radar detection. The size and clothing Bicyclist protection has become an important issue for color of bicycle riders are determined based on the general trafﬁc safety considerations. In the past, efforts were made estimates system (GES)/FARS crash data and the TASI 110- to require bicyclists to wear helmets and adhere to riding car NDD. regulations (MacAlister and Zuby, 2015). Some studies also The remainder of this paper is organized as follows. Section 2 concluded that some bicyclist safety speciﬁc facilities, such introduces the data sources used for generating crash scenarios. as bike routes, bike lanes, bike paths, cycle tracks at Based on the detailed analysis of the crash scenarios, a set of ﬁve roundabouts, could potentially reduce the number of crash geometries is recommended for the evaluation of bicyclist bicycle-related crashes and fatalities. Street lighting, paved AEB systems in Section 3. Section 4 discusses environmental surfaces and low-angled grades are additional factors that factors related to crash scenarios, including lighting conditions, appear to improve bicyclist safety (Reynolds et al., 2009). obscuring objects, vehicle speed and bicyclist speed. Section 5 While the improvement of the road environment can reduce describes the design of the surrogate bicyclist including the the risk of bicyclist crashes, the vehicles equipped with crash determination of the key parameters used in the design of the warning/avoidance systems have also been introduced in surrogate bicyclist. Section 6 presents the experimental setup recent years to reduce the potential bicyclist injuries and for the proposed bicyclist AEB system test. The conclusion is fatalities (Rosen, 2013). These systems are typically referred drawn in Section 7. to as bicyclist pre-collision systems (PCS) or bicyclist automatic emergency braking (AEB) systems. The bicyclist 2. Data sources AEB systems are designed to warn the driver and/or brake Accidentology is used to determine the primary crash scenarios. automatically to help mitigate or avoid imminent bicyclist A crash scenario is deﬁned by three groups of factors: crashes if the driver does not apply brakes in emergency situations. 1 Bicyclist/vehicle crash geometries; To support the development of standardized evaluation of 2 Bicyclist crash environmental factors including lighting the bicyclist AEB systems, a team of researchers in the conditions, obscuring objects, vehicle speed and bicyclist Transportation Active Safety Institute (TASI) at Indiana speed; and University-Purdue University Indianapolis conducted a two- 3 Bicyclist description factors including size, bicyclist year research project in collaboration with Toyota’s clothing color and bicyclist limb motion. Collaborative Safety Research Center. One of the main The test scenario analysis was based on three data sources, the research goals was to develop the test scenarios, test equipment GES, FARS and TASI 110-car NDD. The GES data include a and test procedures for testing and evaluating bicyclist AEB nationally representative sample data set of approximately systems (Yi et al.,2016, 2017). During the same period, the 50,000 police-reported motor vehicle related bicyclist crashes European New Car Assessment Programme (Euro NCAP) was of all severities. FARS is a census of all fatal motor vehicle also developing a bicyclist AEB testing system, named Cyclist- crashes that occurred in the USA. AEB Testing System (CATS). Their goal was to draft the test First, all bicycle-related records in the harmonized GES/FARS protocols, test scenarios and surrogate target for Cyclist-AEB 2010-2011 were extracted, which included approximately systems in 2018 and 2020 (den Camp et al.,2017). 55,000 crashes, 693 fatalities and US$10.08bn in social cost. Considering the fact that many crashes were not AEB relevant, Figure 1 Trends of bicyclist fatalities and fatality rates according to the data set was reconstructed to exclude the following FARS conditions: crashes involving more than one bicyclist; crashes involving many vehicles, heavy vehicles or motorcycles; and ﬁrst harmful event is not the collision with a bicyclist; and the driver backed over or lost control. After removing the irrelevant cases, or cases that are too difﬁcult to simulate, approximately 38,816 crashes per year (about 71.5 per cent of all bicyclist crashes and approximately 16 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 70.5 per cent of all bicyclists involved), 481 fatalities per year crossing road and vehicle along the road paths. “UP” means (approximately 69.4 per cent of all bicyclist fatalities) and US unknown. “XX” is for crashes that are not bicyclist AEB $7.06bn in social cost per year (70.0 per cent of bicyclist system-relevant, such as crashes involving a parked vehicle. injury-related social costs) were retained as AEB-relevant and The second component is vehicle action, where “VS”, “VRT”, “VLT”, “VHO”, “VOT”, “VDO” and “VDT” testable. represent the vehicle going straight, the vehicle right Although the GES/FARS data could provide a reasonable turning, the vehicle left turning, the vehicle head-on estimate of crash geometries, some important details required approaching the bicycle, the vehicle overtaking the bicycle, for creating test scenarios were missing, such as the bicycle the vehicle driving out (stop and drive out without yielding) moving speed, the bicyclist clothing color and the bicycle limb and the vehicle driving through (ignoring potential stops), motion pattern. To determine these parameters, TASI 110-car respectively. The third component is the bicyclist behavior, NDD were used. where “BS”, “BOT”, “BHO”, “BRT” and “BLT” represent The TASI 110-car NDD were collected and processed. the bicyclist going straight, the bicyclist overtaking the The database has naturalistic driving information of 116 vehicle, the bicyclist striking the vehicle head-on, the drivers that was recorded from February 2012 to June 2013. bicyclist right turning and the bicyclist left turning, Each driver completed a 12-month NDD collection process. respectively. All these abbreviations and their deﬁnitions About 90 terabytes of data were collected with near 40,000 h can be found in Table I. and 1.44 million miles of driving information. The NDD These harmonized data with bicycle crash types provide an provided the front view in 1080p resolution video, GPS opportunity to obtain the most detailed crash information locations, 3D acceleration and the vehicle speed of each about bicyclists in both absolute and relative crash geometries. vehicle in a one-year period. Hence, the TASI 110-car NDD To determine the detailed testing scenarios, vehicle pre-crash helped us identify the missing parameters from the GES/ maneuver and bicyclist behavior (Figure 2) are discussed as FARS data. follows. 3. Crash geometries 3.1 PP:VOT, parallel crashes with the vehicle traveling The most important characteristic of the vehicle–bicycle straight and overtaking the bicycle crashes is the relative trajectories of the vehicle and bicycle. The scenarios with the bicycle being overtaken by vehicle Based on the harmonized GES/FARS 2010-2111 crash represent the crashes that do not happen at intersections. data, the basic crash geometries consist of 13 bicycle Consequently, the vehicle speed could be represented by a crossing road scenarios and 11 bicycle parallel to the car function related to the speed limit of the road. The term scenarios, as shown in Figure 2. The coding scheme in BCType of GES/FARS provides details for decomposing Figure 2 is based on three key features of the crash geometry. the PP:VOT into subtypes by incorporating bicycle behavior The ﬁrst component includes the paths of the vehicle and beyond the direction of travel (Figure 3). The subtypes the bicycle before the crash. Where “PP” means bicycle path include the bicycle turning right in front of the vehicle is parallel to vehicle path, and “CP” stands for bicycle (PP:VOT-BRT), the bicycle turning left in front of the Figure 2 Basic crash geometries 17 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 Table I Abbreviations/deﬁnitions 3.2 PP:VHO, vehicle going straight and has head-on collisions with the bicycle Abbreviation Definition Head-on collisions are typically not associated with PP Bicycle path is parallel to vehicle path intersections and both bicyclist and vehicle travel at CP Bicycle crossing road and vehicle along the road path reasonably high speeds (Figure 4). Details for head-on UP Unknown paths crashes include: XX Crashes that are not bicyclist AEB system relevant, Bicyclist is making a right turn (PP:VHO-BRT), where it such as crashes involving a parked vehicle is initially on the opposite side of the road. VS Vehicle going straight Bicyclist is initially traveling on the same side of the road VRT Vehicle right turning as the vehicle but does not leave sufﬁcient gap for the VLT Vehicle left turning vehicle (PP:VHO-BLT). VHO Vehicle head-on approaching the bicycle Vehicle is traveling on the wrong side of the road, or the VOT Vehicle overtaking the bicycle bicycle is traveling on the wrong side of the road VDO Vehicle driving out (stop and drive out without yielding) (PP:VHO-BS). VDT Vehicle driving through (ignoring potential stops) BS Bicyclist going straight Since both mistakes involve the same crash geometries, they BOT Bicyclist overtaking the vehicle are combined into one single scenario. The results are BHO Bicyclist striking the vehicle head-on shown in Table III. It covers about 4.3 per cent of the annual BRT Bicyclist right turning social cost. BLT Bicyclist left turning 3.3 CP:VDO, crossing paths with vehicle going straight without the right of way This type of crashes represents scenarios that the vehicle is Figure 3 Crash geometry of PP:VOT scenarios traveling straight and does not have the right of way (Figure 5). The detailed sub-scenario types are shown in Table IV.This group represents about 13 per cent of the bicycle crashes and less than 1 per cent of the fatalities. Two different sub-scenario types are found: 1) CP:VDO-BS (driver stops and goes and fails to yield) and CP:VDT-BS (driver does not stop), which represent 3.9 and 1.2 per cent of the total social cost, respectively. 3.4 CP:VS, crossing paths with vehicle traveling straight with right of the way This type of crashes represents the scenarios where the vehicle is traveling straight and has the right of way vehicle (PP:VOT-BLT), the bicycle traveling straight and struck from behind by the vehicle (PP:VOT-BS) and the Figure 4 Crash geometry of PP:VHO scenarios bicycle riding out from a midblock location (PP:VOT- BRO). Table II shows the data of crash geometries PP:VOT. The overall social cost in this group is US$1812.22m, covering about 26 per cent of the total annual social cost. In the sub- scenarios, PP:VOT-BS comprises the maximum percentage of the vehicle overtaking crashes; it covers approximately 19 per cent of the total social cost of all bike crashes. It should be noticed that “Fatalities” in Table II is annualized fatalities, and the “per cent” is the percentage of social cost in all crash cases. The unit for social cost is million US dollars. Table II Crashes, fatalities and social cost of PP:VOT Table III Crashes, fatalities and social cost of PP:VHO Scenarios Crashes Fatalities Social cost (%) Scenarios Crashes Fatalities Social cost (%) PP:VOT-BLT 960 29.5 360.83 5.2 PP:VOT-BRO 441 4.5 77.59 1.1 PP:VHO-BHO 510 20.5 221.43 3.2 PP:VOT-BRT 342 6.5 67.95 1.0 PP:VHO-BLT 306 5.0 66.65 1.0 PP:VOT-BS 2,609 118.5 1,311.85 18.7 PP:VHO-BRT 37 1.0 14.96 0.0 Overall PP:VOT 4,352 159.0 1,812.22 26.0 Overall PP:VHO 853 26.5 303.04 4.3 18 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 3.5 PP:VLT, parallel scenarios with the vehicle turning Figure 5 Crash geometry of CP:VDO scenarios left This type of crashes is shown in Figure 7, which has two common sub-scenario types: 1 vehicle turning left and the bicyclist traveling in the same direction as the vehicle before it turns (PP:VLT-BS); and 2 bicyclist on the road or on the sidewalk enters the intersection, while the vehicle turns left into the roadway or a junction (PP:VLT-BHO). These two types of crashes comprise about 8 per cent of all bicyclist crashes and 4.8 per cent of the total social cost (Table VI). Table IV Crashes, fatalities and social cost of CP:VDO Scenarios Crashes Fatalities Social cost (%) 3.6 CP:VLT, crossing scenarios with vehicle turning left The second type of vehicle left-turn scenarios is associated with CP:VDO-BS 4,183 3.5 273.05 3.9 CP:VDT-BS 907 3.5 84.45 1.2 bicycle crossing paths (Figure 8). This type of crashes includes: Overall CP:VDO 5,090 7.0 357.51 5.1 vehicle does not yield to a bicycle (CP:VDL-BS); driver cuts the corner and strikes a bicyclist (CP:VCC-BS); bicycle drives out (CP:VLT-BDO); and other unidentiﬁed cases. (Figure 6). It is expected that the vehicle travels within the speed limit. This type of crashes is very complicated. Except Figure 7 Crash geometry of PP:VLT scenarios for the sub-scenarios of bicycle riding through (CP:VS- BDT) and riding out (CP:VS-BRO), the crashes involve the bicyclist cutting the corner (CP:VS-BCC), the bicyclist swinging wide (CP:VS-BSW), the bicyclist is trapped or there is an obscuring object (CP:VS-BMT) and other cases where the bicyclist has turning errors (CP:VS-BTE). The results are shown in Table V.We can ﬁnd that the scenario of CP:VS-BRO has the highest percentage in terms of the social cost, which covers about 12.5 per cent of the total social cost. Figure 6 Crash geometry of CP:VS scenarios Table VI Crashes, fatalities and social cost of PP:VLT Scenarios Crashes Fatalities Social cost (%) PP:VLT-BHO 3,076 9.0 334.51 4.8 PP:VLT-BS 597 3.0 76.18 1.1 Overall PP:VLT 3,674 12.0 410.69 5.9 Figure 8 Crash geometry of CP:VLT scenarios Table V Crashes, fatalities and social cost of CP:VS Scenarios Crashes Fatalities Social cost (%) CP:VS-BRO 3,628 60 877.58 12.5 CP:VS-BDT 2,236 52 637.88 9.1 CP:VS-BMT 193 2.5 30.22 0.0 CP:VS-BCC 179 2 30.16 0.0 CP:VS-BSW 122 1 16.37 0.0 CP:VS-BTE 12 0.5 5.25 0.0 Overall CP:VS 6,358 116.5 1,592.20 22.7 19 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 As this type of crashes only covers about 1.9 per cent of the total Figure 10 Crash geometry of PP:VRT scenarios social cost, they are not considered in the AEB system evaluation (Table VII). 3.7 CP:VRT, crossing scenarios with vehicle turning right This type of crashes occurs when the vehicle turns right and bicyclist crosses (Figure 9). It covers about 8 per cent of the total social cost. The most frequent scenario in this type is the situation where the vehicle swings too wide as they make the right turn (CP:VSW-BS). It covers 3.1 per cent of the total social cost. The second most frequent crash scenario is CP: VDR-BS, where the vehicle “drives out” while making a right turn and bicyclist goes straight and crosses the road (Table VIII). Table IX Crashes, fatalities and social cost of PP:VRT 3.8 PP:VRT, parallel scenarios with the vehicle turning Scenarios Crashes Fatalities Social cost (%) right PP:VRT-BS 1,735 2.0 153.85 2.2 This type of crashes involves parallel path crashes with the PP:VRT-BHO 874 2.0 71.29 1.0 vehicle turning right. They consist of two bicyclist crash Overall PP:VRT 2,609 4.0 225.14 3.2 scenarios: PP:VRT-BS and PP:VRT-BHO. As both of them have a small percentage of the total social cost (2.2 and 1.0 per cent, respectively), they are not considered in the AEB 3.9 Recommended crash geometries system evaluation (Figure 10)(Table IX). Based on the detailed analysis of the above scenarios, a set of ﬁve crash scenarios is recommended for the evaluation of the Table VII Crashes, fatalities and social cost of CP:VLT bicyclist AEB systems. It involves three parallel crash Scenarios Crashes Fatalities Social cost (%) geometries: 1 parallel paths with the vehicle overtaking the bicycle CP:VCC-BS 1,273 3.0 95.81 1.4 (PP:VOT); CP:VLT-BDO 267 0.5 21.80 0.0 2 parallel paths with the vehicle approaching head-on CP:VDL-BS 287 0.0 14.51 0.0 (PP:VHO); and CP:VLT-Otr 557 0.0 31.77 0.5 3 parallel paths with vehicle turning left and bicycle striking Overall CP:VLT 2,384 3.5 163.89 1.9 vehicle head-on (PP:VLT-BHO). It also involves two crossing crash geometries: 1 crossing paths with vehicle driving out (CP:VDO-BS); Figure 9 Crash geometry of CP:VRT scenarios and 2 crossing paths with vehicle going straight and the bicyclist is riding out (CP:VS-BRO). While the CP:VS-BDT scenario incorporates a large fraction of overall social cost, it is very similar to the CP:VS-BDT scenario; the only difference being whether the bicycle stops or not prior to its failure to yield at the intersection. This allows the inclusion of the CP:VDT-BS scenario where the vehicle is primarily at fault by failing to yield. 4. Crash environmental factors Besides crash geometries, we need to determine the crash environmental factors, which include lighting conditions, Table VIII Crashes, fatalities and social cost of CP:VRT obscuring objects, vehicle speed and bicyclist speed. Scenarios Crashes Fatalities Social cost (%) 4.1 Lighting conditions CP:VDR-BS 3,209 2.0 214.08 3.1 Three lighting levels are considered: daylight, dark-lit (which CP:VSW-BS 3,536 3.5 239.31 3.4 includes dusk and dawn) and dark-unlit. The statistical data CP:VRT-BDO 714 1.0 53.74 0.8 show that a vast majority of bicyclist crashes occur during the CP:VRT-Otr 688 1.5 54.93 0.8 daytime, which cover 61.7 per cent of the total social cost Overall CP:VRT 8,147 8.0 562.06 8.0 (Table X). 20 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 Table X Distributions of different lighting conditions bicyclist cases are obtained through video analysis. Three main scenarios are analyzed: Lighting Crashes Fatalities Social cost (%) 1 bicyclist moving along the road; Daylight 32,945 235.0 4,318.89 61.7 2 bicyclist crossing the road with constant speed Dark-lit 7,397 120.0 1,643.39 23.5 (ride through); and Dark-unlit 1,402 96.5 1,001.65 14.3 3 bicyclist crossing the road from stationary (ride out). Other 72 3.0 39.03 0.6 The bicycle moving speed distributions for the scenarios of moving along the road and ride through crossing the road are 4.2 Obscuring objects shown in Figures 12 and 13, respectively. For the scenario of Our analysis of obscuring objects considers three major types: moving along the road, the average bicyclist moving speed is 5.59 m/s. The 25th and 75th percentile speeds are 4.06 m/s and 1 physical objects between the driver and the bicyclist, such 6.94 m/s, respectively. For the scenario of ride through crossing as a parked car or truck; the road, the average moving speed is 5.23 m/s, and the 25th 2 moving vehicles during a multiple threat/trapped event, and 75th percentile speeds are 3.94 m/s and 6.26 m/s, which mostly happen in crossing pathway geometries; and respectively. The scenario of crossing the road from being 3 glare. stationary is more complex. The overall average moving speed The crashes involving obscuring objects account for less than is 3.5 m/s. To obtain detailed moving behavior, the road is 10 per cent of all crashes (Table XI). The detailed scenarios marked with ﬁve key points: roadside, 25 per cent of the road, suggest that obscuring objects do not play an important role 50 per cent of the road, 75 per cent of the road and another side of the road. The average speeds when crossing the road from except for the scenario of CP:VS-BMT. stationary at 25, 50 and 75 per cent of the road are 2.95 m/s, 3.77 m/s and 3.85 m/s, respectively. The average bicyclist 4.3 Vehicle speed crossing the road acceleration is 1.4 m/s . There is no data source available to describe vehicle speed for bicyclist-related crashes. We estimate the vehicle crash speed by using the speed limit of the road. The distribution of the speed limit for crashes is shown in Figure 11, where we ﬁnd that the most important speed limit for crashes is 25 mph, while the Figure 12 Speed distributions for the bicyclist moving along the road most important speed limits for fatalities are 35 mph and 45 mph. 4.4 Bicyclist speed From the crash databases, it is difﬁcult to estimate bicycle speed during crashes. TASI 110-car NDD were used for ﬁnding bicyclist speed (Fu et al.,2017). From the data, 1,000 Table XI Distributions of different obscuring objects Obstructions Crashes Fatalities Social cost (%) None 37,694 421.5 6,406.53 91.5 Object 2,593 12.5 292.34 4.2 Glare 1,195 11.5 1,89.63 2.7 Other 334 9.0 1,14.47 1.6 Figure 13 Speed distributions for the bicyclist ride through crossing the road Figure 11 Distribution of speed limits for crashes 21 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 5. Surrogate bicyclist design Figure 14 Micro-Doppler effect of pedaling motion Bicyclist crash geometries, bicyclist crash environmental factors and surrogate bicyclist design are the three key variables that affect the performance testing of AEB systems. This section presents the detailed design of the surrogate bicyclist system. 5.1 Representative size of bicycle riders and bicycles According to “Bicycling and Walking in the USA, 2014 Benchmarking Report”, adults between the age of 16 and 64 account for 77 and 68 per cent of all bicyclist fatalities and injuries, respectively (Milne and Melin, 2014). Seniors of age 65 or higher represent 12 per cent of all bicyclist fatalities and 5 per cent of all bicyclist injuries. Children under age of 16 represent 11 per cent of all bicyclist fatalities and 27 per cent of all bicyclist injuries between 2009 and 2011. As bicyclists over the age of 16 cover 89 per cent of fatalities and 73 per cent of injuries, only the adult surrogate bicyclist size is recommended. Moreover, only the male adult-sized bicyclist surrogate is recommended, as male account for 87 per cent of bicyclist 1.01 Hz for the cases of moving along the road (PP-VOT and fatalities and 83 per cent of bicyclist injuries. PP-VHO). For scientiﬁc soundness, a more precise method to To demonstrate the importance of pedaling motion in the determine the size of the surrogate is the weighted 50th performance of bicyclist AEB system, a comparison AEB test of percentile size of male and female adults. Therefore, the the same surrogate bicyclist with two different leg postures (one weighted height of US male and female adults, 173 cm, is straight leg or both bending legs) was conducted [Figure 15(a) recommended as the height of bicycle rider surrogate. As a few and Figure 15(b)]. The test vehicle used was a commercially centimeters height difference might not affect the bicyclist available SUV that has a radar and camera-based bicyclist AEB detection signiﬁcantly, traditional 50th percentile height of US system. The results [plotted in Figure 15(c)] show that the male adults, 175.6 cm (CDC, 2018), is also acceptable as the AEB system performs better for the cases where one leg is height of surrogate bicycle rider. straight than for cases where both legs are bent. The results According to the statistical report of Bicycle Product demonstrate that leg postures do affect the performance of Supplier Association in 2012, the most popular adult bicycles bicyclist AEB systems. The same conclusion was also reported in US market have 26-inch wheel size. The frame types of most adult bicycles are mountain bicycles and road bicycles. Therefore, the wheel size of the adult surrogate bicycle is Figure 15 Comparison of bicyclist AEB system performance with suggested to be 26 inches, and the shape of the adult surrogate different leg postures bicycle is deﬁned as a mix of mountain and road types. 5.2 Limb motion of the bicycle rider Limb motion of bicyclists can be a useful feature for bicyclist detection. Many camera-based pedestrian and bicyclist detection studies emphasize the importance of limb motion for achieving better detection results (Wojek et al., 2009; Takahashi et al., 2010). For the radar-based detection systems, it was also pointed out that the limb motion/pedaling can produce a very noticeable micro-Doppler effect from the front ° ° ° (0 ), back (180 )and 45 -side observation angles, as shown in Figure 14 (Belgiovane and Chen, 2017). There has been some discussion about whether the limb motion/pedaling is required for the surrogate bicyclist. Euro NCAP has adopted the bicyclist surrogate with ﬁxed leg posture (both legs bent) based on their study that suggests that a majority of bicyclists (over 80 per cent) stop pedaling when crossing an intersection in Europe. By examining 484 randomly selected bicyclist video clips in the TASI 110-car NDD (Sherony et al.,2016), it is observed that 83.2 per cent of bicyclists have pedaling motion when crossing the road, and 100 per cent of bicyclists have pedaling motion when moving along the road. The average pedaling frequency is 0.85 Hz for the cases of crossing the road (CP:VDO and CP:VS-BRO) and 22 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 by a study from Euro NCAP (den Camp et al., 2017). The Details of color selection method can be found in den Camp possible reason of performance difference given in den Camp et al. (2017). For the convenience of selecting the color needed, et al. (2017) is that the AEB system uses characteristics from a range of 610 per cent variation of brightness is used to make the 360 pedaling sequence. Based on the above discussions, the acceptable color range. The RGB values and acceptable the leg motion/pedaling capability is recommended for the color ranges for both upper and lower clothing color are shown in Figure 17. proposed surrogate bicyclist design. 5.5 Surrogate bicyclist hardware development 5.3 Wheel rotation The surrogate bicyclist (including a bicycle rider and a bicycle Although it is difﬁcult to know if any camera-based AEB system surrogate) is designed to represent the visual and radar on a production vehicle uses the wheel rotation to detect the characteristics of the real bicyclists in the USA. Therefore, the bicyclist, it is well documented that radar systems use micro- surrogate bicyclist should have similar physical properties with Doppler to detect wheel rotation of bicyclists. The pedaling respect to most sensors used for the bicyclist detection. The motion and the wheel rotation can generate clear micro- parameters identiﬁed and recommended in Sections 5.1-5.4 Doppler responses. The experimental results presented in are considered as part of the requirements for the proposed Belgiovane and Chen (2017) have shown that a rotating wheel surrogate bicyclist design. could produce distinctive periodic micro-Doppler spectral lines In 2015, the ﬁrst generation of the surrogate bicyclist was whose fundamental frequency is related to the vehicle speed, developed by TASI (Yi et al.,2016). The surrogate bicycle and the periodic frequency changes are related to the leg and rider has skin that matches the 77- to 78-GHz radar reﬂectivity wheel rotations. This micro-Doppler effect of a wheel can be of the human skin. By using this skin and realistic body shape, observed in front (0°), back (180°) and 45° side view angles. the radar cross section (RCS) of the surrogate bicycle rider is Figure 16 shows the micro-Doppler measurement of rotating similar to representative real bicycle rider from all 360-degree bicycle wheel of 66 rpm in the back view. The discrete ripples in angles in the view of the 77 GHz automotive radar. To the vertical direction reﬂect the tire thread pattern. harmonize our surrogate bicyclist design with CATS’ design, When both wheels can be detected using micro-Doppler, an we conducted a comparison study by cross-testing and H-shape micro-Doppler signature can be observed (CDN, examining each other’s surrogate bicyclist prototype in 2016. 2018; TNO, 2018). This H-shaped bicycle signature cannot be Based on the results, we modiﬁed our design and developed a observed when the bicycle is ahead of the vehicle and moving new generation of the harmonized surrogate bicyclist, which along the road. consists of three key components: bicycle rider, bicycle and As the automotive radar systems can use micro-Doppler transport sled (Figure 18). Owing to the fact that most adult characteristics for bicycle detection, the wheel rotation is also recommended for the proposed surrogate bicyclist design. Figure 17 Suggested clothing colors for bicyclist AEB systems testing 5.4 Clothing color of bicycle rider Bright- The camera is a common sensor used for bicyclist detection. RGB Darker Lighter Color ness Besides the shape of the bicyclist, the clothing color also values color color variation signiﬁcantly affects the performance of bicyclist AEB systems. Upper Black 57,60,67 49,52,59 65,68,75 To ensure that the clothing color identiﬁcation is not affected clothing (Deep 10% by lighting conditions, the images of 1,905 adult bicyclists not color grey) Lower under shade were ﬁltered from all bicyclists detected in the 44,42,46 37,36,39 51,48,53 clothing Black 10% TASI 110 car naturalistic driving study. For these bicyclists’ color images, the K-means clustering algorithm is applied to ﬁnd the color clusters for both upper and lower cloth colors in the Source: Yi et al. (2017) International Commission on Illumination (CIE) LUV color space. As a result, the black/deep gray combination is suggested to be the most representative clothing color for adult bicyclists. Figure 18 Prototype of the surrogate bicyclist Figure 16 Micro-Doppler measurement of bicycle wheel in 180 observation angle under the rotation speed of 66 rpm 23 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 bicyclists involved in crashes in the USA are male, the 50th Figure 20 Micro-Doppler response of pedaling legs with stationary percentile height of US male population, 175.6 cm, is wheels of the surrogate bicycle considered as the surrogate bike rider’s height. The mannequin for pedestrian PCS evaluation (Yi et al., 2014) is modiﬁed as the bicycle rider. The body and limbs are made to generate realistic human body shape. The harmonized surrogate bicycle has both leg pedaling and wheel rotation to produce the proper micro-Doppler features (Belgiovane and Chen, 2017)and support the camera-based AEB systems (Takahashi et al., 2010). The ﬁnished prototype of the harmonized surrogate bicyclist is shown in Figure 18. The total weight of the surrogate bicycle is 18.4 lbs (8.4 kg), and the total weight of the surrogate bicycle rider is 9 lbs (4.1 kg). The crash testing experiments show that a developed target can handle 45-mph full-speed crash without damage or with minor damage and can be reset in 5 min. 5.6 Radar Cross Section and micro-Doppler effect The far-ﬁeld RCS pattern data of the harmonized surrogate bicyclist are measured. Figure 19(a) shows the comparison between the smoothed (7 average moving) RCS patterns of the US 26-inch mountain bike with a human rider and the prototype harmonized surrogate bicycle and rider mannequin. Figure 19(b) compares the measured RCS patterns of the 26-inch mountain bike and the harmonized surrogate bicycle. It can be seen that the measured RCS data of the developed surrogate bicyclist agree well with that of the real rider plus bicycle in terms of both RCS pattern shape and level in 360 degrees. Figures 20 and 21 show the micro-Doppler measurement of leg pedaling and wheel rotation produced by the developed Figure 21 Micro-Doppler measurement of rotating surrogate wheel from behind surrogate bicyclist. The micro-Doppler features match the real human leg pedaling shown in Figure 14, and the real wheel rotation is shown in Figure 16. Figure 19 Smoothed (7° moving average) RCS patterns for real bicycle + human and mannequin + surrogate bike under the 77 GHz radar 6. Veriﬁcation and automatic emergency braking systems system performance testing Veriﬁcation and bicyclist AEB performance testing were conducted in the summer of 2015 and 2016. A production vehicle equipped with a bicyclist AEB system was used for the veriﬁcation and bicyclist AEB performance testing. A complete set of bicyclist crash scenarios has been presented in Section 3 and Section 4. Among those, however, not every scenario is testable in the ﬁeld because some scenarios are beyond the capability of the existing bicyclist AEB systems based on the vehicle user manual. The setup of the testing system is shown in Figure 22. It consists of: 24 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 Figure 22 Overall test setup differential GPS and data recording equipment installed 5.6 m/s (average speed) and 6.9 m/s (75 percentile) for moving on the test vehicle; along the road scenarios; 5.2 m/s (average) for crossing the road a remotely controlled surrogate bicyclist; with ride through scenarios; and 3.5 m/s for crossing the road a remotely controlled bicycle transport platform; from ride out scenarios. For each vehicle test run, the DGPS two infrared sensors called Start sensor and Stop sensor data, sensor data and video data are captured. All test data have (the Start sensor is used to trigger the start motion of the been processed and used for the AEB system scoring and beneﬁt surrogate bicyclist and the bicycle carrier, and the Stop analysis. Two ﬁeld testing photos for along the road scenario and sensor is only used for the scenario of crossing the road), crossing the road scenario are shown in Figures 23 and 24, to trigger the stop of surrogate bicyclist motion; respectively. a joystick controller for controlling the platform motion The purpose of the testing is not to evaluate the performance direction; and of the AEB system, but to check how to conduct the testing a central control computer. according to the desired test scenarios with the developed surrogate bicyclist and the associated hardware setup and All communication between the components is conducted software tools. wirelessly through a Zigbee network. The differential GPS is used to measure the motion proﬁle 7. Conclusion and the motion speed of the testing vehicle. The data recording equipment is designed to capture all AEB-related signals from The development of testing scenarios and surrogate bicyclist the vehicle, which include brake pedal motion signal, brake system for the evaluation of bicyclist AEB systems has been light signal and AEB audio warning. The surrogate bicyclist is described in this paper. Based on the analysis of the harmonized mounted on a sled. The bicycle carrier is designed to pull the GES/FARS crash data, a set of ﬁve crash scenarios has been sled moving along or crossing the road. recommended for the performance testing of bicyclist AEB About 300 test runs were conducted and data were recorded. systems. Combined with TASI 110-car NDD, crash Table XII shows the actual testing scenarios obtained from the environmental factors including lighting conditions, obscuring vehicle user manual. The range of vehicle speed is from 10 mph objects, vehicle speed and bicyclist speed have been determined. to 60 mph in an increment of 5 mph. The bicycle moving speed The surrogate bicyclist was designed to represent the visual and and mannequin leg pedaling speed are set at different speeds radar characteristics of the real bicyclists in the USA. The height based on the testing scenarios, i.e. 4.1 m/s (25 percentile), of the bicycle rider mannequin is 173 cm, representing the Table XII Scenarios for ﬁeld testing Vehicle motion Bicyclist motion Bicyclist speed Light condition Vehicle speed Straight Away from the Vehicle 5.6 m/s (average) Daylight 10 to 60 mph with 5 mph increment 6.9 m/s (75%) 4.1 m/s (25%) Crossing 5.2 m/s (Ride through) 3.5 m/s (Ride out) Stationary 0 25 Bicyclist automatic emergency braking systems Journal of Intelligent and Connected Vehicles Qiang Yi et al. Volume 1 · Number 1 · 2018 · 15–27 CDC (2018), “Anthropometric reference data for children and Figure 23 Along the road testing scenario adults: United States, 2011-2014”, available at: www.cdc. gov/nchs/data/series/sr_03/sr03_039.pdf CDN (2018), “(Euro NCAP) TEST PROTOCOL – AEB VRU systems”, available at: https://cdn.euroncap.com/ media/26997/euro-ncap-aeb-vru-test-protocol-v20.pdf den Camp, O.O., van Montfort, S., Uittenbogaard, J. and Welten, J. (2017), “Cyclisttargetand test setupfor evaluation of cyclist-autonomous emergency braking”, International Journal of Automotive Technology,Vol.18 No. 6, pp. 1085-1097. Fu, L., Tian, R., Li, L., Chen, Y. and Sherony, R. (2017), “Bicycle speed analysis for assessment of bicyclist pre- collision system”, 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Figure 24 Crossing the road with constant speed testing scenario Trafﬁc Safety Administration, Washington, DC. MacAlister, A. and Zuby, D.S. (2015), Cyclist Crash Scenarios and Factors Relevant to the Design of Cyclist Detection Systems, Insurance Institute for Highway Safety, Arlington, VA. Milne, A. and Melin, M. (2014), “Bicycling and walking in the United States: 2014 benchmarking report”. NHTSA (2016), “Fatal motor vehicle crashes: overview”, available at: www.nhtsa.gov/press-releases/usdot-releases- 2016-fatal-trafﬁc-crash-data NHTSA (2018), “Trafﬁc safety facts, bicyclists and other cyclists”, available at: https://crashstats.nhtsa.dot.gov/Api/ Public/Publication/812382 Reynolds, C.C., Harris, M.A., Teschke, K., Cripton, P.A. and Winters, M. (2009), “The impact of transportation infrastructure on bicycling injuries and crashes: a review weighted height of 50th percentile US male and female adults. of the literature”, Environmental Health, Vol.8No.1, The sizeand shapeofthe surrogate bicycleweredetermined p. 47. as 26-inch (660-mm) diameter wheel and mountain/road Rosen, E. (2013), “Autonomous emergency braking for bicycle frame, respectively. Both leg pedaling motion and vulnerable road users”, Proceedings of IRCOBI Conference, wheel rotation are suggested to produce the proper micro- pp. 618-627. Doppler features and support the camera-based AEB Sherony, R., Tian, R., Chien, S., Fu, L., Chen, Y. and systems. Based on the analysis of the clothing color from Takahashi, H. (2016), “Pedestrian/bicyclist limb motion 1,905 bicyclists in the TASI 110-car naturalistic driving analysis from 110-car TASI video data for autonomous study, black color has been determined as the representative emergency braking testing surrogate development”, SAE color for both upper clothing (RGB: 57,60,67) and lower International Journal of Transportation Safety,Vol. 4 No.1, clothing (RGB: 44,42,46). 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(2014), “Mannequin development for bicyclist for pre-collision system evaluation”, 2017 IEEE pedestrian pre-Collision System evaluation”, 2014 IEEE Intelligent Vehicles Symposium (IV), IEEE, Los Angeles, CA, 17th International Conference on Intelligent Transportation pp. 304-309. Systems (ITSC), IEEE, pp. 1626-1631. Yi, Q., Chien, S., Brink, J., Niu, W., Li, L., Chen, Y., Chen, C. C., Sherony, R. and Takahashi, H., (2016), “Development of bicycle surrogate for bicyclist pre-collision system Corresponding author evaluation”. Lingxi Li can be contacted at: LL7@iupui.edu For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: email@example.com
Journal of Intelligent and Connected Vehicles – Emerald Publishing
Published: Oct 2, 2018
Keywords: Bicyclist; Surrogate bicyclist; Automatic emergency braking (AEB); Crash scenarios; Crash testing; Naturalistic driving; Radar cross section (RCS); Micro-Doppler
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