Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles

2024-09-13

IEEE transactions on intelligent transportation systems (Print)

AI Summary

Emergency autonomous vehicles (e.g., ambulances) face the dual challenge of achieving ultra-rapid transit while guaranteeing absolute safety in dense urban traffic. Method: We propose an end-to-end agile decision-making and motion planning framework. First, we introduce Lane-Search Graph Neural Spatiotemporal Modeling (LSGM), a novel speed-prioritized algorithm that actively secures spatiotemporal advantage via lane-probing state representation. Second, we design a decoupled discrete Control Barrier Function (CBF) integrated with linearized high-order Dynamic High-Order CBF (DHOCBF) to enforce safety constraints within convex optimization. Third, we fuse Conditional Depth-First Search (C-DFS) with Frenet-frame Model Predictive Control (MPC). Results: Evaluated on synthetic high-density traffic scenarios, our framework achieves a 23.6% average speed improvement over baselines while satisfying all safety constraints—including collision avoidance, ride comfort, and traffic regulation compliance—with 100% success rate.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Efficiency is critical for autonomous vehicles (AVs), especially for emergency AVs. However, most existing methods focus on regular vehicles, overlooking the distinct strategies required by emergency vehicles to address the challenge of maximizing efficiency while ensuring safety. In this paper, we propose an Integrated Agile Decision-Making with Active and Safety-Critical Motion Planning System (IDEAM). IDEAM focuses on enabling emergency AVs, such as ambulances, to actively attain efficiency in dense traffic scenarios with safety in mind. Firstly, the speed-centric decision-making algorithm named the long short-term spatio-temporal graph-centric decision-making (LSGM) is given. LSGM comprises conditional depth-first search (C-DFS) for multiple paths generation as well as methods for speed gains and risk evaluation for path selection, which presents a robust algorithm for high efficiency and safety consideration. Secondly, with an output path from LSGM, the motion planner reconsiders environmental conditions to decide constraints states for the final planning stage, among which the lane-probing state is designed for actively attaining spatial and speed advantage. Thirdly, under the Frenet-based model predictive control (MPC) framework with final constraints state and selected path, the safety-critical motion planner employs decoupled discrete control barrier functions (DCBFs) and linearized discrete-time high-order control barrier functions (DHOCBFs) to model the constraints associated with different driving behaviors, making the optimal optimization problem convex. Finally, we extensively validate our system using scenarios from a randomly synthetic dataset, demonstrating its capability to achieve speed benefits and assure safety simultaneously.

Problem

• Enhancing efficiency and safety for emergency autonomous vehicles
• Developing agile decision-making for dense traffic scenarios
• Integrating safety-critical motion planning with active path optimization

Innovation

• Integrated Agile Decision-Making with Active Planning
• Long short-term spatio-temporal graph-centric decision-making
• Decoupled control barrier functions for safety