A massive database developed by Deepen AI and the WMG group at Warwick University is boosting testing of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) for driverless cars.
The Safety Pool Scenario Database provides a wide range of scenarios in different operating conditions that can be used by governments, industry and academia alike to test and benchmark Automated Driving Systems (ADSs) and use insights to inform policy and regulatory guidelines. Initial scenarios have been generated using a hybrid methodology developed by WMG using both knowledge-based and data-based approaches.
The Safety Pool Scenario Database will allow organisations to create scenarios in their own libraries, collaborate with other organisations via both shared and public libraries and enable the public to submit challenging real world scenarios. Enabling scenarios to be matched to specific environments and operating conditions means that trials and tests can be undertaken in the simulated environment, controlled test facilities and on public roads, with evidence from each environment being used to inform our understanding of safe behaviours, bringing Autonomous Vehicles closer to market at pace.
To ensure that Autonomous Vehicles are road-ready and will be safer than the average human driver, it has been suggested that they must be tested on 11 billion miles of roads, which is not possible in the real world. Testing on virtual roads in simulation environments is vital for manufacturers and government bodies to ensure safe behaviours and assure that driverless cars are a positive influence on road safety.
“Safety of automated driving systems is a hard research challenge and can only to solved by national and international collaboration and knowledge sharing,” said Dr Siddartha Khastgir, from WMG, University of Warwick who has been developing the database over the last seven years. “With the launch of Safety Pool Scenario Database, we are inching closer to seeing automated driving systems on the roads. Testing and validating automated driving systems transparently in an integrated simulation-based