FST Graduate Research Topic

Research Topic: AGI Research and Development: Ego-Vehicle Perception & Control System in Autonomous Vehicles
Suggested By: Harris Wang
Last Update: September 28th, 2025

AGI Research and Development: Ego-Vehicle Perception & Control System  in Autonomous Vehicles Constrained Object Hierarchies (COH) provide a neuroscience-inspired theoretical framework for modeling Artificial General Intelligence (AGI). As a flexible and robust foundation, COH supports the representation of general intelligent systems across diverse domains. Formally, a COH is defined as a 9-tuple: O = (C, A, M, N, E, I, T, G, D) Where: C: Components – sub-objects organized hierarchically A: Attributes – state variables describing object properties M: Methods – executable actions associated with components N: Neural Components – adaptive models for learning and inference E: Embedding Neural Component – semantic representation of the COH I: Identity Constraints – rules ensuring object integrity T: Trigger Constraints – event-condition-action rules for dynamic behavior G: Goal Constraints – optimization objectives guiding intelligent behavior D: Constraint Daemons – real-time monitors enforcing system constraints   Based on the COH framework, the modelling process and result of Ego-Vehicle Perception & Control System are as follows: System Description and Significance Autonomous vehicles are a critical application domain where intelligent systems must operate safely in complex, dynamic environments. The ego-vehicle system integrates perception, decision-making, and control to navigate while avoiding hazards and complying with traffic laws. Its significance lies in transforming transportation through improved safety and efficiency, while addressing substantial challenges in real-time intelligence.   Analysis and Design The system requires hierarchical decomposition into functional modules—Perception, Localization, Planning, and Control—with precise coordination. Safety is paramount, enforced through identity constraints that prevent collisions. The design must handle uncertain sensor data via neural components while maintaining predictable behavior through constraint governance. The embedding component provides situational awareness by integrating information across modules.   Modeling Solution C: {PerceptionModule, LocalizationModule, PlanningModule, ControlModule} A: {current_pose, velocity, sensor_data, trajectory_plan, obstacle_list} M: {filter_sensor_data(), update_belief_state(), calculate_trajectory(), adjust_steering()} N: {NeuralObjectDetector, NeuralMotionPredictor} E: Situation embedding unifying module outputs for holistic scene understanding I: safety_bubble (minimum safe distance maintained at all times) T: IF obstacle_detected WITHIN trajectory THEN invoke emergency_braking AND replan_path G: Minimize travel_time AND Maximize passenger_comfort AND Adhere_to_traffic_laws D: CollisionAvoidanceDaemon monitoring obstacle_list and trajectory This COH model captures essential intelligence requirements: hierarchical modularity, neural adaptability, safety constraints, and multi-objective optimization. The constraint daemon ensures proactive hazard avoidance through continuous monitoring.     You may choose this topic and pursue one of the following routes: 📝 Essay Survey existing methodologies for modeling, designing, and implementing similar intelligent systems. Include: Literature synthesis COH framework analysis Critical evaluation of competing approaches 🛠️ Project Design and implement the system using the COH model. Include: System architecture and design Development and testing Demonstration of core functionality 🎓 Thesis Explore COH’s theoretical foundations in depth. Include: Proposed enhancements or extensions Implementation and evaluation Development of an advanced intelligent system grounded in COH principles