AutoSceCraft: Generate Various Driving Scenarios from Scratch for Autonomous Driving Systems

Autonomous Driving Systems (ADS) are safety-critical. Abundant and various driving scenarios are required to train accurate and robust models, and comprehensively test each module of autonomous driving systems (i.e., perception, tracking, prediction, planning, and control modules). However, collecting driving scenario data from the real physical world is expensive and inefficient. Most existing works generate simulated driving scenarios by varying the behaviors of dynamic objects on simple road networks (e.g., highways), while the influence of roadside structures and scenarios with complex road networks are not considered. This paper proposes a novel driving scenario generation approach, Automated Scenario Crafting (AutoSceCraft), to automatically produce abundant driving scenarios containing various road networks, traffic rules, roadside structures, and dynamic objects at low cost. To validate the effectiveness and efficiency of our proposed framework, AutoSceCraft is integrated into three popular driving simulators, including SMARTS, esmini, and CARLA. Numerical experiments and scenario visualization results show that AutoSceCraft can generate effectively and efficiently various driving scenarios from scratch for testing and training various modules (including perception, prediction, and planning modules) within autonomous driving systems.