This repository contains source code for search-based software testing of a pedestrian detection system using the virtual prototyping platform ESI Pro-SiVIC. We hope that the example code provided can help future developers and researchers getting started with software testing using ESI Pro-SiVIC. The examples use Data Distribution Service (DDS) for communication between ESI Pro-SiVIC and MATLAB and Simulink, resepctively.

The corresponding research article is currently in preparation (Dec 2019).

The repository contains four main parts:

• sbst_pedestrian_detection_system: An adaptation of the original NSGA-II algorithm for search-based testing in ESI Pro-SiVIC.
• utils: Various scripts that can be used to reproduce our results.
• example_prosivic_matlab: Examples of DDS communication between ESI Pro-SiVIC and MATLAB applications.
• example_prosivic_simulink: An example of DDS communication between ESI Pro-SiVIC and Simulink blocks.

This is the core component of the repository, demonstrating how NSGA-II can be used to generate critical test scenarios for a pedestrian detection system simulated using ESI Pro-SiVIC.

The pedestrian detection system (PDS) under test in the research publication is a proprietary Simulink solution that we cannot publish on GitHub. The PDS uses a combination of computer vision and radar signal characteristics to detect pedestrians in front of the vehicle. To support replication of our work, we provide a mock implementation of the PDS component in Simulink. Our mock implementation provides a random value for the detection (0 or 1) and a constant time to collision (4 s). While the detection value is not used in the objective functions (described next), the constant time to collision will effectively remove one of the three objective functions.

The implementation shows how NSGA-II can be for multi-objective optimization, in this case to generate test scenarios for the given scene that minimize three objective functions:

• OF1 = the minimum distance between the pedestrian and the car (min_dist)
• OF2 = the minimum time to collision (min_ttc)
• OF3 = the minimum distance between the pedestrian and the acute warning area in front of the car (min_dist_awa)

Note that all objective functions are considered equally important. We refer to the result from optimizing the three objective functions as a critical scenario.

The function used to calculate OF3 (calc_obj_funcs.m) refers to three positions relative to the acute warning area as shown in the figure below.

To test the pedestrian detection system, a simple scene has been implemented in ESI Pro-SiVIC. The scene enables a replication of the solution presented by Ben Abdessalem et al. (2016) implemented in PreScan. The scene contains a car driving on a straight road and a pedestrian crossing the street from the right. There are no objects along the road, and the driving conditions are normal. Test scenarios are created for this scene by setting five independent test parameters within fixed ranges, i.e., the test input:

1. the x coordinate of the pedestrian (ped_x)
2. the y coordinate of the pedestrian (ped_y)
3. the orientation of the pedestrian (ped_orient)
4. the speed of the pedestrian (ped_speed)
5. the speed of the car (car_speed)

In a nutshell, our implementation does the following:

1. Randomly create an initial population (NSGA-II)
2. Run simulations for the initial population (ESI Pro-SiVIC)
3. Evaluate the objective functions for the initial population (NSGA-II)
4. Sort the initial population (NSGA-II)
5. Select mates using tournament selection (NSGA-II)
6. Perform crossover (NSGA-II)
7. Insert mutations (NSGA-II)
8. Run a simulation for child 1 (ESI Pro-SiVIC)
9. Evaluate the objective functions for child 1 (NSGA-II)
10. Run a simulation for child 2 (ESI Pro-SiVIC)
11. Evaluate the objective functions for child 2 (NSGA-II)
12. Select the best individuals based on elitism and crowding distance (NSGA-II)
13. Write the results to file

The main files in the root folder are:

• run_NSGAII.m: Main file for running NSGA-II to search for test input representing critical scenarios.
• mock_pedestrian_detection_system.slx: Simulink implementation with a mocked PDS. Communicates with ESI Pro-SiVIC using DDS.
• run_from_file.m: Read a set of test scenarios from a csv-file and run them in ESI Pro-SiVIC.
• run_from_file_theory.m: Read a set of test scenarios from a csv-file and calculate what min_distshould be assuming an ideal model.

The supporting files are:

• config.json: Contains a DDS configuration that must match the ESI Pro-SiVIC installation.
• ProSiVIC_RemoteCommands.dll:
• ProSiVIC_DDS.mex: Function that calls the corresponding C++ implementation
• `sendCommand.mex: Function that calls the corresponding C++ implementation

Furthermore, the four subfolders contain the following:

• genetic_algo: A refactored version of the original algorithm NSGA-II tailored for generation of test cases representing critical test scenarios. NSGA-II was originally developed by the Kanpur Genetic Algorithm Labarotary http://www.iitk.ac.in/kangal/ We hope that the refactored version will be easier to understand for non-experts.
• scripts: Two ESI Pro-SiVIC scripts. First, the ESI Pro-SiVIC scene that replicates the PreScan scene by Ben Abdessalem et al. (2016). Second, a script to turn on shadows for each body part of the ESI Pro-SiVIC pedestrian.
• utils: Three utility functions called by the main MATLAB scripts.

Follow the steps int the sections below to reproduce our study on using NSGA-II with ESI Pro-SiVIC for test case generation.

• MATLAB installed with the Computer Vision Toolbox and the DSP System Toolbox. We used version R2019a.
• ESI Pro-SiVIC with a license that enables level 2 sensors. We used version 2018.
• The contents of the AN_interface_MatlabSimulink.zip archive (available in the installation directory \ESI Group\Pro-SiVIC\2018.0.1\applicationNotes) must be extracted and added to the path in MATLAB.
• Configure ESI Pro-SiVIC to use TCP for DDS communication with Domain ID 15. Probably UDP will work instead of TCP, and that might solve issues with DCPSInfoRepo (described below).

1. Copy the file scripts/prescan_repl_minimal.script to the ESI Pro-SiVIC folder used for loading scenes.
2. Start ESI Pro-SiVIC
3. Start MATLAB
4. Set adas-pro-sivic\sbst_pedestrian_detection_system as the current folder in MATLAB.
5. Run run_NSGAII.m

The following variables are used to tune NSGA-II:

• time_budget: Time budget allowed by NSGA-II to find solutions.
• population_size: Size of the initial population. The same number of solutions will be identified.
• nbr_mutations: Number of mutations inserted after crossover.

The following variables are used to set the input ranges for the ESI Pro-SiVIC scene:

• min_ranges = array of minimum values for ped_x, ped_y, ped_orient, ped_speed, car_speed, respectively.
• max_ranges = array of maximum values for ped_x, ped_y, ped_orient, ped_speed, car_speed, respectively.

• Sometimes the background service DCPSInforRepo does not start properly with ESI Pro-SiVIC. Try starting it manually from the bin folder in the ESI Pro-SiVIC installation using the command DCPSInfoRepo -o f:/temp/repo.ior -ORBListenEndpoints iiop://:4242 (replace the path to match the location of your local repo.ior) Run the following command in MATLAB and verify in the Pro-SiVIC log that the command has been received: ret=sendCommand('PLAY','localhost')

Supporting scripts to reproduce our results. Instructions will follow when the paper manuscript has been finished.

The code can be used to get started with DDS communication between ESI Pro-SiVIC and MATLAB. The code is provided as is, but should be fairly straightforward even without documentation beyond code comments.

The code can be used to get started with DDS communication between ESI Pro-SiVIC and Simulink. The code is provided as is, but should be fairly straightforward even without documentation beyond code comments.

• Ben Abdessalem, R., Nejati, S., Briand, L.C. and Stifter, T. Testing advanced driver assistance systems using multi-objective search and neural networks. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, pp. 63-74, 2016.