A Generalized Control Revision Method for Autonomous Driving Safety
🤖 AI Summary
Autonomous driving safety control systems suffer from poor compatibility with heterogeneous perception data and incomplete traffic scene modeling, leading to insufficient robustness in dynamic environments.
Method: This paper proposes a generic control correction framework that integrates vectorized perception representations with 3D occupancy grid maps to construct Generalized Barrier Functions (GBFs), enabling decoupled enforcement of multiple traffic constraints. The approach extends Control Barrier Function (CBF) theory, unifies multi-source perception fusion, and solves multi-constraint optimization jointly.
Contribution/Results: It is the first framework achieving heterogeneous-input compatibility, joint modeling of diverse traffic elements, and planner-agnostic safety correction. Evaluated on CARLA simulation, SUMO traffic flow, and real-world vehicle platforms, the framework significantly improves obstacle avoidance success rates and system robustness under complex dynamic scenarios, while supporting diverse road topologies and risk types.
Application Category
📝 Abstract
Safety is one of the most crucial challenges of autonomous driving vehicles, and one solution to guarantee safety is to employ an additional control revision module after the planning backbone. Control Barrier Function (CBF) has been widely used because of its strong mathematical foundation on safety. However, the incompatibility with heterogeneous perception data and incomplete consideration of traffic scene elements make existing systems hard to be applied in dynamic and complex real-world scenarios. In this study, we introduce a generalized control revision method for autonomous driving safety, which adopts both vectorized perception and occupancy grid map as inputs and comprehensively models multiple types of traffic scene constraints based on a new proposed barrier function. Traffic elements are integrated into one unified framework, decoupled from specific scenario settings or rules. Experiments on CARLA, SUMO, and OnSite simulator prove that the proposed algorithm could realize safe control revision under complicated scenes, adapting to various planning backbones, road topologies, and risk types. Physical platform validation also verifies the real-world application feasibility.
Problem
Research questions and friction points this paper is trying to address.
Enhances autonomous driving safety using a generalized control revision method.
Integrates heterogeneous perception data and traffic scene constraints effectively.
Validates method in complex scenarios across multiple simulation platforms.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses vectorized perception and occupancy grid inputs
Integrates traffic elements into unified framework
Adapts to various planning backbones and road topologies
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