This book addresses higher–lower level decision autonomy for autonomous vehicles, and discusses the addition of a novel architecture to cover both levels. The proposed framework’s performance and stability are subsequently investigated by employing different meta-heuristic algorithms. The performance of the proposed architecture is shown to be largely independent of the algorithms employed; the use of diverse algorithms (subjected to the real-time performance of the algorithm) does not negatively affect the system’s real-time performance. By analyzing the simulation results, the book demonstrates that the proposed model provides perfect mission timing and task management, while also guaranteeing secure deployment. Although mainly intended as a research work, the book’s review chapters and the new approaches developed here are also suitable for use in courses for advanced undergraduate or graduate students.
Highlights comprehensive and new perspectives to autonomous vehicle’s real-time decision making in critical situationsDetails operation diagrams and schematics of the framework’s different components using tables, figures, and flowchartsDiscusses how a high-level decision maker and a local a low-level action generator can simultaneously plan and carry out a mission while allowing for dynamic maneuvering in a cluttered and uncertain environment
Highlights innovative perspectives on real-time decision making for autonomous vehicles in critical situations Details operational diagrams and schematics of the framework’s various components using tables, figures, and flowcharts Discusses how a high-level decision maker and a local low-level action generator can simultaneously plan and carry out a mission while allowing for dynamic maneuvering in a cluttered and uncertain environment
Somaiyeh MahmoudZadeh
Autonomous mission planning Decision making/autonomy Situational awareness Motion/path planning Meta-heuristics evolutionary algorithms Vehicle routing Ant Colony Optimisation (ACO) Biogeography-Based Optimisation (BBO) Firefly Optimization Algorithm (FOA) Imperialist Competitive Algorithm (ICA) Particle Swarm Optimization (PSO) Autonomous Underwater Vehicles (AUV) Operational ocean environment