Figures (17)  Tables (8)

    • Figure 1. 

      Schematic of the test scenario construction process. ① selected dynamic and static scene parameters; ② filtered test scenarios.

    • Figure 2. 

      Multidimensional decomposition of scenario elements.

    • Figure 3. 

      Workflow of the variable-strength PICT combination testing method.

    • Figure 4. 

      Data collection area for the SPMD.

    • Figure 5. 

      PDF and CDF of SIC for extracted scenarios. (a) Car-following scenario. (b) Cut-in scenario.

    • Figure 6. 

      Random forest parameter training process. (a) The number of decision trees for the car-following scenario. (b) The number of feature variables for the car-following scenario. (c) The number of decision trees for the cut-in car scenario. (d) The number of feature variables for the cut-in car scenario.

    • Figure 7. 

      Ranking of feature importance in RF. (a) Car-following scenario. (b) Cut-in scenario.

    • Figure 8. 

      Spearman correlation analysis results. (a) Car-following scenario. (b) Cut-in scenario. The meanings of the numbers in the figure are as follows: ① ego vehicle speed, ② ego vehicle acceleration, ③ ego vehicle yaw rate, ④ leading vehicle speed, ⑤ leading vehicle acceleration, ⑥ relative longitudinal distance, ⑦ relative speed, ⑧ relative lateral distance, and ⑨ SIC.

    • Figure 9. 

      Probability distribution of basic dynamic parameters for Grand 3 car-following scenario samples. (a) Relative longitudinal distance. (b) Relative lateral distance. (c) Relative speed. (d) Leading vehicle speed. (e) Leading vehicle acceleration.

    • Figure 10. 

      Probability distribution of basic dynamic parameters for Grand 3 cut-in scenario samples. (a) Relative longitudinal distance. (b) Relative lateral distance. (c) Relative speed. (d) Leading vehicle speed. (e) Leading vehicle acceleration.

    • Figure 11. 

      Generation of car-following scenarios. (a) Original scenario. (b) PICT combined generated test scenario. (c) Filtered scenario.

    • Figure 12. 

      Generation of cut-in scenarios. (a) Original scenario. (b) PICT combined generated test scenario. (c) Filtered scenario.

    • Figure 13. 

      Elbow plot of clustering. (a) Car-following scenario. (b) Cut-in scenario.

    • Figure 14. 

      Silhouette coefficient plot for clustering. (a) Car-following scenario. (b) Cut-in scenario.

    • Figure 15. 

      Fifteen representative simulation test scenarios. (a) Car-following scenario. (b) Cut-in scenario.

    • Figure 16. 

      Simulation test results for car-following Scenario 1. (a) Ego vehicle speed. (b) Ego vehicle acceleration. (c) Relative distance.

    • Figure 17. 

      Simulation test results for cut-in Scenario 1. (a) Ego vehicle speed. (b) Ego vehicle acceleration. (c) Relative distance.

    • Static scenario elements Parameter value
      Natural environment elements Weather
      conditions      		 Rainy Small, medium, large
      Snowy Small, medium, large
      Foggy Small, medium, large
      Clear day −
      Illumination condition Day, night
      Traffic environment elements Number of lane lines 2, 3, 4
      Traffic flow Low, medium, high

      Table 1. 

      Parameter set for static scenario elements.

    • Screening index Screening condition
      TTC TTC < 2 s
      THW 20 m/s < Vx < 40 m/s THW < 0.01 × Vx + 0.1
      Vx ≥ 40 m/s THW ≤ 0.5 s

      Table 2. 

      Scenario filtering criteria.

    • SIC range Safety impact level Number of scenarios
      SIC ≤ 0.7539 Grand 3 2,214
      0.7539 < SIC ≤ 0.8025 Grand 2 3,233
      0.8025 < SIC ≤ 0.8395 Grand 1 7,923
      SIC > 0.8395 Grand 0 5,209

      Table 3. 

      Car-following scenario risk level classification.

    • SIC range Safety impact level Number of scenarios
      SIC ≤ 0.7600 Grand 3 428
      0.7600 < SIC ≤ 0.8004 Grand 2 724
      0.8004 < SIC ≤ 0.8488 Grand 1 2,986
      SIC > 0.8488 Grand 0 1,680

      Table 4. 

      Cut-in scenario risk level classification.

    • Fitting parameter Mean ($ \mu $) Standard deviation ($ \sigma $)
      Relative longitudinal distance 23.48 26.58
      Relative lateral distance 0.03 1.03
      Relative speed −2.74 5.03
      Leading vehicle speed 38.59 24.75
      Leading vehicle acceleration 0.0011 0.0225

      Table 5. 

      Normal distribution fit of key parameters for car-following scenarios.

    • Fitting parameter Mean ($ \mu $) Standard deviation ($ \sigma $)
      Relative longitudinal distance 11.03 16.47
      Relative lateral distance 1.52 0.02
      Relative speed −2.27 3.79
      Leading vehicle speed 40.98 21.18
      Leading vehicle acceleration 0.0012 0.0196

      Table 6. 

      Normal distribution fit of key parameters for cut-in scenarios.

    • Scenario elements Parameter value
      Car-following Cut-in
      Relative longitudinal distance (1.5, 46.5) 1.5 m discrete (3, 33) 3 m discrete
      Relative lateral distance 0 (1, 3) 0.5 m/s discrete
      Relative speed (−10, 5) 1 m/s discrete (−10, 5) 1 m/s discrete
      Leading vehicle speed (15, 60) 5 m/s discrete (15, 60) 5 m/s discrete
      Leading vehicle acceleration 0 0

      Table 7. 

      Vehicle dynamic information elements set.

    • Scenario
      type      		 Scenario WC IC NL TF LD
      (m)      		 LAD
      (m)      		 RS
      (m/s)      		 LS
      (m/s)      		 LA
      (m/s2)
      Car-following 1 CD N 6 H 19.5 0 0 30 0
      2 R-M D 4 L 4.5 0 3 15 0
      3 CD N 4 M 34.5 0 0 25 0
      4 S-S D 6 M 10.5 0 −6 50 0
      5 CD N 2 L 7.5 0 −9 35 0
      6 R-S D 2 H 13.5 0 −10 55 0
      7 S-M N 6 L 22.5 0 0 55 0
      8 R-M N 4 H 4.5 0 −4 40 0
      9 F-S D 6 H 1.5 0 −8 30 0
      Cut-in 1 S-L N 2 L 18 1.5 0 22 0
      2 CD D 6 M 6 2.5 0 60 0
      3 R-M N 4 M 15 1 −10 60 0
      4 S-S N 6 H 24 2.5 −8 17 0
      5 R-L D 2 H 12 3 −10 40 0
      6 F-L D 4 L 33 2 0 20 0
      WC, weather conditions; IC, illumination condition; NL, number of lane lines; TF, traffic flow; LD, relative longitudinal distance; LAD, relative lateral distance; RS, relative speed; LS, leading vehicle speed; LA, leading vehicle acceleration. In WC, CD represents clear day. If shown in the form 'a-b', a represents weather (R, S, F, for rainy, snowy, foggy, respectively), and b represents severity (S, M, L, for small, medium, large, respectively). In IC, D and N represent day and night, respectively. In TF, L, M, and H represent low, medium, and high, respectively.

      Table 8. 

      Fifteen types of typical test scenarios.