# Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles arXiv: https://arxiv.org/abs/2409.08665 ยท HTML: https://mlsanity.com/paper/2409.08665/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. - Builds on 24 of its 71 references that are in the map. - First posted on arXiv 13 Sep 2024. ## Placed in - End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/) ## Builds on (most cited first, 24 of 24) - [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016) - [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023) - [Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function](https://mlsanity.com/paper/2007.11718/) (2020) - [View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums](https://mlsanity.com/paper/2110.07288/) (2021) - [Control Barrier Functions for Systems with High Relative Degree](https://mlsanity.com/paper/1903.04706/) (2019) - [SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.05370/) (2023) - [MSN: Multi-Style Network for Trajectory Prediction](https://mlsanity.com/paper/2107.00932/) (2021) - [CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces](https://mlsanity.com/paper/2202.08506/) (2022) - [An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors](https://mlsanity.com/paper/2005.11059/) (2020) - [Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles](https://mlsanity.com/paper/2310.18891/) (2023) - [PythonRobotics: a Python code collection of robotics algorithms](https://mlsanity.com/paper/1808.10703/) (2018) - [Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts](https://mlsanity.com/paper/1810.00829/) (2018) - [A Learning-based Discretionary Lane-Change Decision-Making Model with Driving Style Awareness](https://mlsanity.com/paper/2010.09533/) (2020) - [Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change](https://mlsanity.com/paper/2103.12382/) (2021) - [Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning](https://mlsanity.com/paper/2401.11148/) (2024) - [Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic](https://mlsanity.com/paper/2308.05929/) (2023) - [An Autonomous Driving Framework for Long-term Decision-making and Short-term Trajectory Planning on Frenet Space](https://mlsanity.com/paper/2011.13099/) (2020) - [Autonomous Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee using Control Barrier Functions](https://mlsanity.com/paper/2112.06435/) (2021) - [Automated Lane Merging via Game Theory and Branch Model Predictive Control](https://mlsanity.com/paper/2311.14916/) (2023) - [A Parameter Privacy-Preserving Strategy for Mixed-Autonomy Platoon Control](https://mlsanity.com/paper/2401.15561/) (2024) - [A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward](https://mlsanity.com/paper/2009.11905/) (2020) - [Feasibility Enhancement of Constrained Receding Horizon Control Using Generalized Control Barrier Function](https://mlsanity.com/paper/2102.13304/) (2021) - [Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method](https://mlsanity.com/paper/2102.12124/) (2021) - [Iterative Convex Optimization for Model Predictive Control with Discrete-Time High-Order Control Barrier Functions](https://mlsanity.com/paper/2210.04361/) (2022) ## Built on by (most cited first, 3 of 3) - [Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control](https://mlsanity.com/paper/2411.10031/) (2024) - [LA-RL: Language Action-guided Reinforcement Learning with Safety Guarantees for Autonomous Highway Driving](https://mlsanity.com/paper/2512.05686/) (2025) - [Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware](https://mlsanity.com/paper/2601.03904/) (2026) ## Abstract (arXiv) 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.