Testing autonomous driving vehicles refers to a quality assurance process in which diverse validation activities are performed based on well-defined quality assurance standards and assessment criteria. In a quality validation process, unmanned autonomous driving vehicles are validated at different levels (component, integration, and system) based on the pre-defined quality requirements to assure system quality in algorithms, functions, components, behaviors, connectivity, sensing, performance, intelligence, and decision makings in diverse contexts and conditions. To reduce the cost and increase the testing efficiency in the autonomous driving industry, simulation testing receives increasing attention and effort in recent years. A high-fidelity simulation software usually contains the mathematical representations of the environment, the dynamics of autonomous vehicles and surrounding vehicles, the sensors models, etc., at different levels, and is needed to facilitate the testing and development of autonomous driving systems. In order to perform efficient simulation testing, techniques for optimizing and accelerating testing processes are in great demand.
This challenge is set up as a platform to address this demand, and advocate the importance and need of quality validation and automation for autonomous driverless cars. This platform provides a global competition opportunity for international student teams and professional teams to develop diverse simulation testing techniques and approaches in test scenario generation and automation.
In this challenge, LGSVL simulator will be used to support simulation testing and execution. LGSVL simulator is a Unity-based autonomous vehicle simulator developed by LG Electronics America R&D Center. The LGSVL simulator can generate various realistic 3D environments by adjusting environmental parameters including maps, weather, traffic, and pedestrians. It can also simulate different sensor outputs, including camera, Lidar, radar, ultrasonic, etc., and lots of virtual sensors to generate ground truth data (e.g. depth, semantic/instance segmentation, 2D/3D bounding box, etc.). Users can generate and test arbitrary edge case scenarios and simulate billions of miles. LGSVL simulator is fully integrated with the open-source platforms Baidu Apollo and Autoware. In this challenge, teams will use LGSVL integrated with Baidu Apollo to generate and evaluate test cases.
The details about LGSVL could be found in the following URLs:
AV Simulation Test Challenge:
The challenge consists of two phases:
1. Training and team selection. During this phase, training materials including tutorial videos, documents, and online Q&A sessions will be delivered to the teams so that the teams will get familiar with LGSVL simulator.
Available training materials:
Two Q&A / demo sessions
2. Competition. During this phase, teams use the traffic accident/crash database to create scenarios, automation scripts, and UI, and submit for evaluation. The report  done by the National Highway Traffic Safety Administration discussed the most accident scenario due to human operation according to the data they collected in the past years.
 https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/pre-crash_scenario_typology-final_pdf _version_5-2-07.pdf
Challenge Tasks For Each Team:
1. Form a team and make your challenge registration via specified challenge platform. Each team can have up to 5 members.
2. Attend challenge training sessions
3. Create specified test simulation scenarios and develop your executive test scripts
4. First phase deliverable - Work on deliverable #1 and submit your challenge project artifacts to the specified challenge platform.
5. Second phase deliverable - Work on deliverable #2 and submit your challenge project artifacts to the specified challenge platform.
6. Final challenge demo and evaluation for selected final teams.
7. Final paper submission and presentation for selected final teams.
Deliverable #1 - Develop specified simulation test scenarios and scripts for a specified route. For details, please refer to the deliverable #1 specification document. (AVTest-Challenge-Deliverable1)
Deliverable #2 – Develop AV simulation test scenarios and scripts based on the given requirements. (see AVTest-Challenge-Deliverable2)
The results will be evaluated based on
1) Test simulation automation in test simulation modeling, auto-script generation, auto-result validation, auto-coverage analysis, and auto-report
2) Models and methodology
3) Simulation demos based on specified routes, scenarios, and rules
4) The number of test simulation scenarios