  Autonomous Vehicles (AVs) need an accurate and up-to-date representation of
the environment for safe navigation. Traditional methods, which often rely on
detailed environmental representations constructed offline, struggle in
dynamically changing environments or when dealing with outdated maps.
Consequently, there is a pressing need for real-time solutions that can
integrate diverse data sources and adapt to the current situation. An existing
framework that addresses these challenges is SDS (situation-aware drivable
space). However, SDS faces several limitations, including its use of a
non-standard output representation, its choice of encoding objects as points,
restricting representation of more complex geometries like road lanes, and the
fact that its methodology has been validated only with simulated or heavily
post-processed data. This work builds upon SDS and introduces SDS++, designed
to overcome SDS's shortcomings while preserving its benefits. SDS++ has been
rigorously validated not only in simulations but also with unrefined vehicle
data, and it is integrated with a model predictive control (MPC)-based planner
to verify its advantages for the planning task. The results demonstrate that
SDS++ significantly enhances trajectory planning capabilities, providing
increased robustness against localization noise, and enabling the planning of
trajectories that adapt to the current driving context.
