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Other Applied Science Fields
Transportation
Autonomous Vehicles (Self-Driving Cars)
1. Introduction to Autonomous Vehicles
2. Sensor Technologies and Hardware Components
3. Environmental Perception and Scene Understanding
4. Localization and Mapping
5. Path Planning and Decision Making
6. Vehicle Control Systems
7. Artificial Intelligence and Machine Learning
8. System Integration and Communication
9. Testing, Validation, and Verification
10. Safety, Security, and Reliability
11. Regulatory, Legal, and Societal Aspects
5.
Path Planning and Decision Making
5.1.
Global Path Planning
5.1.1.
Route Planning Fundamentals
5.1.1.1.
Graph-Based Representations
5.1.1.2.
Cost Function Design
5.1.1.3.
Multi-Objective Optimization
5.1.2.
Search Algorithms
5.1.2.1.
Dijkstra's Algorithm
5.1.2.2.
A* Algorithm
5.1.2.3.
D* Algorithm for Dynamic Environments
5.1.3.
Hierarchical Planning
5.1.3.1.
Multi-Resolution Planning
5.1.3.2.
Coarse-to-Fine Approaches
5.1.3.3.
Road Network Abstraction
5.2.
Behavioral Planning
5.2.1.
Driving Behavior Modeling
5.2.1.1.
Rule-Based Behavior Systems
5.2.1.2.
Finite State Machines
5.2.1.3.
Behavior Trees
5.2.2.
Scenario-Based Decision Making
5.2.2.1.
Scenario Classification
5.2.2.2.
Context-Aware Planning
5.2.2.3.
Risk Assessment and Management
5.2.3.
Maneuver Planning
5.2.3.1.
Lane Change Decision Making
5.2.3.2.
Intersection Navigation
5.2.3.3.
Merging and Yielding Strategies
5.2.3.4.
Overtaking and Following Behavior
5.2.4.
Learning-Based Behavioral Planning
5.2.4.1.
Imitation Learning
5.2.4.2.
Reinforcement Learning for Driving
5.2.4.3.
Inverse Reinforcement Learning
5.3.
Local Motion Planning
5.3.1.
Trajectory Generation
5.3.1.1.
Polynomial Trajectory Planning
5.3.1.2.
Spline-Based Methods
5.3.1.3.
Bezier Curve Trajectories
5.3.2.
Sampling-Based Planning
5.3.2.1.
Rapidly-Exploring Random Trees (RRT)
5.3.2.2.
Probabilistic Roadmaps (PRM)
5.3.2.3.
Lattice-Based Planning
5.3.3.
Optimization-Based Planning
5.3.3.1.
Model Predictive Control (MPC)
5.3.3.2.
Quadratic Programming Approaches
5.3.3.3.
Nonlinear Optimization Methods
5.3.4.
Collision Avoidance
5.3.4.1.
Static Obstacle Avoidance
5.3.4.2.
Dynamic Obstacle Prediction
5.3.4.3.
Safety Corridor Generation
5.3.5.
Real-Time Constraints
5.3.5.1.
Computational Complexity Management
5.3.5.2.
Anytime Algorithms
5.3.5.3.
Hierarchical Planning Approaches
5.4.
Multi-Agent Planning
5.4.1.
Interaction-Aware Planning
5.4.1.1.
Game-Theoretic Approaches
5.4.1.2.
Social Force Models
5.4.1.3.
Cooperative Planning
5.4.2.
Prediction and Planning Integration
5.4.2.1.
Joint Prediction-Planning Frameworks
5.4.2.2.
Contingency Planning
5.4.2.3.
Risk-Aware Planning
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4. Localization and Mapping
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6. Vehicle Control Systems