SDS++: Online Situation-Aware Drivable Space Estimation for Automated Driving

Autonomous vehicles require accurate, real-time environmental representations for safe navigation. Traditional methods relying on detailed offline maps struggle in dynamic environments with outdated data. Thus, real-time solutions integrating diverse data sources and adapting to current situations are essential. An existing framework addressing this challenge is the situation-aware drivable space (SDS). However, SDS faces limitations such as simplified object representation, non-standard output, and validation only on simulated or high-quality data. This paper introduces SDS++, an advanced framework for drivable space estimation addressing these limitations. Key improvements include incorporating complex geometries, providing a more flexible and accurate drivable space, and adopting a standardized output representation. Methodologically, SDS++ operates in two main steps. First, it formulates a Graph-based SLAM optimization from semantic objects and determines the most likely environment, leveraging a novel custom factor for optimizing curved line features. Second, it estimates drivable space using artificial potential fields (APFs) constructed from domain knowledge through implicit and sigmoid functions. SDS++ offers several benefits. The novel factor allows the incorporation of complex geometries such as road lanes and edges. Constructing APFs from domain knowledge provides a flexible drivable space that adapts to the driving situation. Additionally, APFs created from implicit and sigmoid functions offer higher accuracy than typical bi-Gaussian representations. SDS++ has been rigorously validated in simulations and through experiments in a real vehicle with unrefined data. Integration with a model predictive control (MPC)-based planner demonstrates significant enhancements in trajectory planning, showcasing SDS++'s robustness against localization noise and ability to adapt trajectories to the driving context.