Autonomous Vehicle System Design

The Autonomous Vehicle System Design interview assesses your ability to design an end-to-end autonomous driving system that perceives its environment, makes safe driving decisions, and controls vehicle motion in real time. This mock interview evaluates your understanding of sensor fusion, perception modeling, path planning, control systems, and scalable deployment of autonomous fleets.

You’ll learn to design systems that integrate multi-sensor inputs (camera, LiDAR, radar, GPS, IMU) to detect objects, track movement, and predict trajectories in complex traffic environments. The interview emphasizes building robust perception pipelines, designing decision and planning modules, and leveraging machine learning models for tasks such as object recognition, lane detection, and behavioral prediction.

Goals of the Interview:

1. Define the problem and requirements for an autonomous driving system.
2. Design perception and data processing pipelines for sensor fusion.
3. Create ML-based models for detection, prediction, and decision-making.
4. Plan, evaluate, and optimize control strategies for real-world driving.
5. Deploy, monitor, and scale the self-driving system for fleet operation.