CS Driving Final Exam Note Sheet: Ace Your Exam with This Comprehensive Guide
Preparing for your Computer Science (CS) driving final exam can be daunting. This note sheet aims to provide a concise yet comprehensive overview of key concepts, helping you confidently tackle the exam. Remember, this is a supplement to your class notes and textbook; it's not a replacement!
This guide assumes a broad understanding of fundamental CS concepts related to driving, likely encompassing topics such as autonomous vehicles, traffic simulation, path planning, and related algorithms. Let's break down essential areas.
1. Autonomous Vehicle Systems Architecture
Understanding the architecture of a self-driving car is crucial. Key components include:
- Sensors: LiDAR, radar, cameras, ultrasonic sensors – each with strengths and weaknesses regarding range, accuracy, and weather conditions. Know their limitations and how they complement each other.
- Perception: This involves processing sensor data to create a 3D model of the environment, including object detection, classification, and tracking. Familiarize yourself with common algorithms used in this stage (e.g., object detection using convolutional neural networks).
- Localization & Mapping: Determining the vehicle's precise location and building a map of the surroundings. GPS, inertial measurement units (IMUs), and sensor fusion play crucial roles. Understand concepts like SLAM (Simultaneous Localization and Mapping).
- Planning: This involves deciding where the vehicle should go and how to get there. This could include path planning algorithms (e.g., A*, Dijkstra's) and motion planning (considering obstacles and dynamic environments).
- Control: Executing the plan by controlling the vehicle's actuators (steering, throttle, brakes). This often involves control systems theory and feedback loops.
2. Path Planning Algorithms
Several algorithms are employed for path planning in autonomous vehicles. Be prepared to explain and compare:
- A Search:* A heuristic search algorithm that efficiently finds the shortest path between two points. Understand its components: heuristic function, cost function, and open/closed lists.
- Dijkstra's Algorithm: Another shortest path algorithm, but less efficient than A* for large graphs. Know the difference between A* and Dijkstra's.
- RRT (Rapidly-exploring Random Trees): Suitable for high-dimensional spaces and complex environments. Understand its probabilistic nature and how it explores the search space.
3. Traffic Simulation and Modeling
Understanding traffic flow is critical for autonomous vehicle development. Key concepts include:
- Microscopic Simulation: Simulating individual vehicles and their interactions.
- Macroscopic Simulation: Modeling traffic flow as a continuous fluid.
- Traffic Models: Familiarize yourself with common traffic models, such as the car-following model and cellular automata models. Understand their assumptions and limitations.
4. Sensor Fusion and Data Integration
Self-driving cars rely on fusing data from multiple sensors to achieve a robust and accurate perception of the environment. Understand how sensor fusion techniques combine data from different sources and address uncertainties. Kalman filters are commonly used; be prepared to discuss their function.
5. Ethical Considerations in Autonomous Driving
This is an increasingly important aspect of the field. Be prepared to discuss ethical dilemmas related to accident avoidance and decision-making in critical situations (e.g., the trolley problem).
6. Common Challenges in Autonomous Driving
Be ready to address some of the significant challenges facing the development of fully autonomous vehicles:
- Adverse Weather Conditions: How do sensors perform in rain, snow, or fog?
- Unpredictable Human Behavior: How do you handle unexpected actions from pedestrians or other drivers?
- Robustness and Safety: How do you ensure the system is reliable and safe in all scenarios?
- Computational Resources: The processing power required for real-time decision making.
This note sheet provides a foundational overview. Ensure you thoroughly review your lecture notes, textbooks, and any assigned readings. Good luck on your exam!
