Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing
Abstract
:1. Introduction
- The development of a new and effective methodology to search for critical NLOS scenarios from a large scenario space. This involved defining a critical scenario as used in this paper and included the development of a methodology to evaluate critical scenarios for NLOS cases. This has been performed by combining several indicators, for example, time to collision (TTC) and total stopping time (TST), as presented in Section 3. This is followed by searching for critical scenarios within the scenario search space through the unique use of a genetic algorithm.
- The subsequent implementation framework that simulates the scenarios selected by the GA. This platform incorporates vehicular communication.
2. Related Work
2.1. Automotive Software Testing
2.2. Metrics
2.3. Evolutionary Algorithm
3. Methodology
3.1. Critical Scenarios
- The position of both dynamic objects must be within NLOS (i.e., the ego vehicle’s on-board sensors have not detected the CAV) from each other.
- There must exist a point of interaction between the trajectories of both dynamic objects, which is also known as the collision point (CP).
- The time taken for both dynamic objects to reach the CP must be within TTC.
- The time taken for the ego vehicle to perceive and react to the oncoming dynamic object and reach a velocity of zero is known as the TST.
- A scenario is therefore critical if TTC < TST or within a safety parameter.
3.2. Simulation Environment
3.3. Genetic Algorithm
- Position of the ego vehicle [XEgo, YEgo, ZEgo].
- Position of the CAV [XCAV, YCAV, ZCAV].
- The position is based on bounding box range as shown in Figure 2. Upper and lower limits for the X and Y position of the ego vehicle and CAV are within NLOS range. As mentioned above, this has been determined through simulation.
- Speed of the ego vehicle.
- Speed of the CAV.
Algorithm 1: Genetic algorithm for NLOS critical scenario selection |
Input: Population size, n Maximum number of iterations, MAX Output: Critical scenario |
Begin: Generate initial population (randomly) of n chromosomes Yi (i = 1, 2, …, n) Set iteration counter t = 0 Compute the fitness values of each chromosome while (t < MAX) Step 1: The individuals are ranked according to fitness value Step 2: The elitism rate is checked and, depending on the value, the appropriate number of individuals are taken over to the next generation Step 3: Select a pair of chromosomes from the current generation based on fitness Step 4: Apply crossover operation on the selected pair with crossover probability Step 5: Select a chromosome from the current generation based on fitness Step 6: Apply mutation on the selected individual with mutation probability Step 7: Replace the old population with a newly generated population Step 8: Increment the current iteration t by 1 end while return the critical scenario end |
- Category 1:
- Category 2:
- Category 3:
- Category 4:
4. Results
- Population Size: 50.
- Number of Generations: 100.
- Crossover Rate: 0.85.
- Mutation Rate: 0.1.
- Elitism Rate: 0.2.
5. Discussion
6. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Indicator | Description | Collision Type | Reference |
---|---|---|---|
Time-to-collision (TTC) or Time-measured-to-collision (TMTC) | The time it would take for the vehicles to collide if they continued on their current paths at their current speeds. | Rear-end collision, weaving/turning, colliding with objects/parked vehicles, crossing and colliding with a pedestrian. | [54,55] |
Time exposed time-to-collision (TET) | Sum of all times (during the time period under consideration) when a driver approaches a front vehicle with a TTC value less than TTC. | As a result of a rear-end collision, turning/weaving, hitting objects/parked vehicles, crossing, hitting a pedestrian and vehicle–vehicle collision. | [56] |
Time integrated time-to-collision (TIT) | The integral when TTC is below the threshold. | As a result of a rear-end collision, turning/weaving, hitting objects/parked vehicles, crossing, hitting a pedestrian and vehicle–vehicle collision. | [56] |
Modified time-to-collision (MTTC) | Models that were modified to account for all possible longitudinal conflict scenarios caused by acceleration or deceleration discrepancies. | As a result of a rear-end collision, turning/weaving, hitting objects/parked vehicles, crossing, hitting a pedestrian and vehicle–vehicle collision. | [57,58] |
Crash index (CI) | The effect of collision speed on the kinetic energy involved. | As a result of a rear-end collision, turning/weaving, hitting objects/parked vehicles, crossing, hitting a pedestrian and vehicle–vehicle collision. | [57] |
Headway (H) | The time it takes for the front of the leading vehicle to pass a point on the road and for the front of the following vehicle to pass the same point. | As a result of a rear-end collision, turning/weaving, hitting objects/parked vehicles, crossing, hitting a pedestrian and vehicle–vehicle collision. | [59,60] |
Time-to-accident (TA) | The time-to-accident (TA) is the amount of time it takes for an accident to occur from the time one of the road users begins an evasive action and continues at the same speed and direction. | As a result of a rear-end collision, turning/weaving, hitting objects/parked vehicles, crossing, hitting a pedestrian and vehicle–vehicle collision. | [61,62] |
Post-encroachment time (PET) | The time between the moment that a road user (vehicle) leaves the area of potential collision and the other road user arrives at the collision area. | Mostly for right-angle or crossing crashes that result in a pedestrian being hit. Head-on collision when merging/diverging (to a certain extent). | [63,64] |
U.K Speed Limit (mph) | Scenario No. | Best Chromosome | Fitness Value | |||
---|---|---|---|---|---|---|
Ego Speed (m/s) | CAV Speed (m/s) | Ego Initial Position [x, y, z] (m) | CAV Initial Position [x, y, z] (m) | |||
20 | 1 | 17.12 | 16.67 | [13.91, 0.97, 0] | [37.26, −24.12, 0] | 264.50 |
2 | 20.39 | 17.17 | [10.86, 1.41, 0] | [38.37, −22.81, 0] | 261.94 | |
3 | 15.87 | 17.52 | [13.97, 1.69, 0] | [38.37, −26.86, 0] | 248.93 | |
4 | 11.31 | 14.31 | [15.24, 1.25, 0] | [38.12, −26.79, 0] | 247.71 | |
5 | 16.99 | 13.81 | [14.59, 1.33, 0] | [38.35, −21.87, 0] | 282.01 | |
30 | 6 | 17.66 | 16.14 | [13.94, 1.96, 0] | [37.65, −25.83, 0] | 226.72 |
7 | 13.44 | 3.62 | [13.91, 1.82, 0] | [38.57, −26.99, 0] | 227.47 | |
8 | 19.36 | 18.42 | [15.17, 2.13, 0] | [38.35, −22.87, 0] | 291.26 | |
9 | 15.84 | 13.87 | [15.46, 1.68, 0] | [38.24, −25.00, 0] | 250.67 | |
10 | 13.08 | 15.04 | [16.74, 1.76, 0] | [37.81, −23.86, 0] | 194.25 | |
50 | 11 | 28.39 | 24.10 | [15.11, 1.32, 0] | [37.30, −24.77, 0] | 137.22 |
12 | 25.85 | 24.21 | [14.67, 1.60, 0] | [38.54, −25.51, 0] | 151.71 | |
13 | 24.18 | 28.68 | [15.28, 1.51, 0] | [38.46, −25.80, 0] | 158.10 | |
14 | 27.96 | 24.15 | [15.42, 1.66, 0] | [38.01, −22.36, 0] | 147.22 | |
15 | 25.03 | 22.31 | [11.98, 1.96, 0] | [38.61, −27.08, 0] | 142.73 | |
60 | 16 | 39.26 | 39.17 | [14.02, 1.47, 0] | [37.86, −22.98, 0] | 111.79 |
17 | 24.75 | 27.11 | [16.46, 2.05, 0] | [37.59, −27.42, 0] | 130.46 | |
18 | 25.67 | 24.02 | [16.68, 1.53, 0] | [38.04, −26.41, 0] | 138.12 | |
19 | 35.58 | 32.86 | [16.87, 1.14, 0] | [37.30, −23.20, 0] | 107.88 | |
20 | 28.30 | 23.88 | [14.28, 2.08, 0] | [37.61, −25.60, 0] | 137.09 | |
70 | 21 | 37.37 | 34.69 | [12.13, 2.14, 0] | [38.61, −27.60, 0] | 107.47 |
22 | 20.12 | 22.11 | [15.13, 1.56, 0] | [38.12, −27.66, 0] | 178.85 | |
23 | 35.18 | 34.11 | [15.85, 1.62, 0] | [38.70, −27.38, 0] | 173.33 | |
24 | 19.53 | 23.00 | [17.13, 1.87, 0] | [38.73, −26.59, 0] | 175.19 | |
25 | 21.77 | 20.92 | [15.37, 1.16, 0] | [38.57, −26.94, 0] | 165.69 |
Scenario Number | Distance to Collision without Communication (m) | Distance to Collision with Communication (m) |
---|---|---|
1 | 2.52 | 17.88 |
2 | 2.42 | 17.63 |
3 | 2.96 | 16.64 |
4 | 1.39 | 14.36 |
5 | 2.52 | 18.13 |
6 | 3.64 | 17.58 |
7 | 5.24 | 18.85 |
8 | 4.76 | 17.31 |
9 | 5.21 | 18.92 |
10 | 3.23 | 17.28 |
11 | 9.25 | 23.25 |
12 | 3.00 | 16.32 |
13 | 2.76 | 14.66 |
14 | 3.95 | 17.92 |
15 | 4.01 | 18.10 |
16 | 5.16 | 20.33 |
17 | 7.55 | 19.48 |
18 | 6.60 | 17.61 |
19 | 5.42 | 18.15 |
20 | 6.02 | 18.37 |
21 | 8.71 | 20.85 |
22 | 7.39 | 20.74 |
23 | 2.60 | 14.43 |
24 | 4.14 | 17.06 |
25 | 5.61 | 21.49 |
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Allidina, T.; Deka, L.; Paluszczyszyn, D.; Elizondo, D. Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing. Software 2022, 1, 244-264. https://doi.org/10.3390/software1030011
Allidina T, Deka L, Paluszczyszyn D, Elizondo D. Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing. Software. 2022; 1(3):244-264. https://doi.org/10.3390/software1030011
Chicago/Turabian StyleAllidina, Tanvir, Lipika Deka, Daniel Paluszczyszyn, and David Elizondo. 2022. "Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing" Software 1, no. 3: 244-264. https://doi.org/10.3390/software1030011