Autonomous Driving: Overview
Autonomous driving refers to self-driving vehicles that can perceive their environment and move safely with little or no human input. For IT professionals, this field combines AI, sensor fusion, edge computing, and high-speed networking.
Key Vocabulary
- Liability: Legal responsibility (Haftung).
- Sensor Fusion: Combining data from radar, lidar, and cameras.
- Latency: Delay in data processing, critical for safety.
SAE Levels of Automation
The SAE (Society of Automotive Engineers) defines six levels of driving automation:
Level 0-2: Driver Assist
Human monitors the environment. Includes features like lane-keep assist and adaptive cruise control.
Level 3-5: Automated
The system monitors the environment. Level 5 requires no steering wheel or pedals.
Task for you:
Can you identify the difference? In Level 3, the driver must be ready to intervene. In Level 4, the car can handle most situations itself.
Advantages & Disadvantages
Pros (Advantages)
- Safety: Reduction of accidents caused by human error (distraction, fatigue).
- Efficiency: Optimized traffic flow and lower fuel consumption/emissions.
- Mobility: Increased independence for the elderly or disabled.
Cons (Disadvantages)
- Cybersecurity: Vulnerability to hacking and data theft.
- Job Loss: Impact on professional drivers (trucks, taxis).
- Cost: High initial investment in infrastructure and sensors.
The "Trolley Problem" in IT
As an IT specialist, you might write the code that decides what happens in an unavoidable accident. This is known as the ethical algorithm.
Scenario:
An autonomous car faces a sudden obstacle. It must choose between hitting a group of pedestrians or swerving into a wall, potentially harming the passenger. Who is responsible for the code's decision? The programmer, the manufacturer, or the owner?
Knowledge Check
Please answer the following questions to complete the module.