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

TL;DR

SDS++ is a real-time, situation-aware drivable space estimation method for autonomous vehicles that improves upon previous models by handling complex geometries, integrating diverse data, and validating with real vehicle data to enhance planning robustness.

Contribution

This paper introduces SDS++, an improved framework for environment representation in autonomous driving, overcoming SDS limitations with real-world validation and integration with planning systems.

Findings

Enhanced trajectory planning robustness

Better adaptation to current driving context

Validated with real vehicle data

Abstract

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environmental representations constructed offline, struggle in dynamically changing environments or when dealing with outdated maps.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.