Parameter Estimation and Simulation of Driving Datasets

Sammanfattning: The development of autonomous driving in recent years has been in full swing and one of the aspects that Autonomous Vehicles (AVs) should always focus on is safety. Although the corresponding technology has gradually matured, and AVs have performed well in a large number of tests, people are still uncertain whether AVs can cope with all possible situations. This world is complex and ever-changing, experiencing countless disturbances every moment, and according to The Butterfly Effect, even the most insignificant small disturbance may set off a huge storm in the near future. If AVs really enter people’s daily lives, they will inevitably encounter many unexpected situations that have never been experienced before. Thus how to ensure that AVs can handle these well has become the most important issue at the moment. It is necessary to give the Automated Driving System (ADS) sufficient challenges during training and testing for acceptable safety and stability. However, dangerous and extreme driving scenarios in the real world are very rare, and it is also very expensive for such a test to be carried out in reality. Therefore, artificially creating a series of critical driving scenarios then training and testing the ADS in a simulation environment has become the current mainstream solution. This thesis project builds a complete framework for the automatic generation, simulation, and analysis of safety-critical driving scenarios. First, the specified scenarios and features are sequentially extracted from the naturalistic driving dataset through pre-defined rules; then a Density Estimation Model is adopted to learn the features, trying to find the distribution of the specified scenarios; after the distribution is obtained, synthetic driving scenarios can be obtained by sampling. Finally, visualize these synthetic scenarios via simulation for safety assessment and data analysis.