Autonomous vehicles simulation research project
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About
About
Nanyang Technological University and Alibaba Self-driving Lab are collaborating on autonomous driving technology research. which aims to develop reliable, interpretable, and verifiable autonomous simulation data solutions. The current research directions are as follows:
Autonomous driving data mining and clustering
Simulation scenario modeling and generation
Safety scenario analysis and reconstruction
Fault injection and adversarial attacks
Accelerated longtail scenario generation
Interactive scenario generation
We sincerely welcome more scholars to join our research work .
Some publicly available rearch
Some publicly available rearch
——HD Map-based Functional Safety Testing of Autonomous vehicles
Scenario-based testing has been the de facto method for the functional safety verification of autonomous vehicles.
The term "scenario" is commonly defined as:
"... a quantitative description of the relevant characteristics and activities and/or goals of the ego vehicle(s), the static environment, the dynamic environment, and all events that are relevant to the ego vehicle(s) within the time interval between the first and the last relevant event." (Gelder, E. D. et al. 2022).
A layered structure of scenario elements has been proposed as shown on the right.
Overview of scenario layered model (Bagschik et al. 2018)
From the layered model it can be seen that the road level (L1) and Traffic Infrastructures (L2) lays the foundation for scenario elements of higher levels (e.g. L3, L4).
Hence, L1 and L2 (i.e. HD maps) are the basics for the scenario diversity and efficiency.
However, it is resource-consuming to construct high quality HD maps, which are, as a result, often overlooked during test scenario construction.
In collaboration with Nanyang Technological University and Alibaba Autonomous Driving Lab, we have conducted the following research aimed at filling the above gaps. An overview of the scene hierarchy model (Bagschik et al. 2018) and the last relevant event. (Gelder, ED, et al., 2022). High-definition map-based functional safety testing of autonomous vehicles, Yun Tang
Topology coverage-Guided Testing
Collisoin Avoidance Testing
HD Map Generation
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