Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'autonomous vehicles' AI and what AI technologies do they combine?
⚠ Common exam trap
Many candidates confuse a single, narrow AI feature (like automatic parking or traffic routing) with the comprehensive integration of multiple AI technologies required for full autonomous driving, leading them to select options that describe simpler, isolated AI workloads.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Combining computer vision, sensor fusion, localisation, behaviour prediction, and path planning AI
Autonomous vehicles represent a complex AI workload that integrates multiple AI technologies to perceive the environment, understand context, and make safe driving decisions. Option B is correct because it specifically lists the core AI technologies—computer vision for object detection, sensor fusion for combining data from cameras, LiDAR, and radar, localization for precise positioning, behavior prediction for anticipating actions of other road users, and path planning for determining the optimal route—that are essential for a vehicle to operate without human intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AI that automatically controls traffic lights to reduce congestion at intersections
Why it's wrong here
Adaptive traffic-light control is an infrastructure-level smart-city application that coordinates signal timing across many vehicles to reduce network congestion. It perceives aggregated traffic flow through sensors or cameras at intersections, not the instantaneous surroundings of one vehicle, and it passively affects flow rather than making driving decisions. Because it optimizes a shared road network from outside the vehicle, it is not autonomous-vehicle AI.
- ✓
Combining computer vision, sensor fusion, localisation, behaviour prediction, and path planning AI
Why this is correct
Full self-driving requires an end-to-end, real-time AI system that integrates multiple disciplines: computer vision for scene understanding, sensor fusion for combining camera/lidar/radar, localisation to pinpoint the vehicle on a map, behavior prediction to forecast pedestrians and other drivers, and path planning to choose safe trajectories. These components work together continuously for perception–prediction–planning, culminating in vehicle-control commands. This complete pipeline, rather than any single assistive feature, is what defines autonomous vehicle AI.
- ✗
AI that automatically parallel parks a car using sensors and pre-programmed rules
Why it's wrong here
Automated parallel parking is a constrained driver-assistance feature (SAE Level 1/2) that uses ultrasonic sensors and predefined geometric maneuvers to slot the car into a spot at low speed. It does not require generalized perception, behavior prediction, or path planning across real road environments, and it cannot handle intersections, pedestrians, or lane changes. Thus it is only a narrow automation task, not the integrated AI stack of a self-driving vehicle.
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Using AI to optimise traffic routing in GPS navigation applications
Why it's wrong here
GPS traffic routing is a route-optimization problem that uses graph algorithms and live traffic data to recommend a path from A to B, typically running on a smartphone or cloud service. It operates at the trip-planning layer, outside the vehicle, and has no sensor perception, motion control, or real-time hazard response. Therefore it is navigation telematics, not autonomous-vehicle driving AI, which must perceive and safely control the vehicle itself.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
Key term
Prediction
Prediction is the process of using data and algorithms to forecast future outcomes or identify patterns without explicit programming for each scenario.
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