How Vehicles Perceive the World — Sensors & Fusion in Autonomous Driving

Humans rely on five senses to interpret the world. Autonomous vehicles rely on more—and they need to process everything in milliseconds. Cameras, radar, lidar, ultrasonic sensors, GPS, and inertial measurement units (IMUs) work together to form a high-resolution, 360° view of the vehicle's surroundings.

But seeing is not enough. For autonomy to work, machines must understand. That’s where sensor fusion enters the picture – a core pillar of safe and scalable autonomous driving. In this article, we explore the sensing stack, its challenges, and why perception isn't just about detecting objects, but interpreting the world with surgical precision.

The Autonomous "Senses": Key Sensor Types Explained

Autonomous systems use a combination of overlapping sensor types to perceive their environment:

• Cameras
  – Detect color, texture, signage, lane markings
  – Strong in visual classification tasks (pedestrians, signals)
  – Weak in fog, glare, or low light
• Radar (Radio Detection and Ranging)
  – Measures distance and velocity with high accuracy
  – Performs well in bad weather
  – Lower resolution than cameras
• Lidar (Light Detection and Ranging)
  – Creates 3D maps with centimeter precision
  – Excellent for depth and object contouring
  – Expensive and sensitive to environmental conditions
• Ultrasonic Sensors
  – Useful for close-range detection (e.g. parking)
  – Cheap and reliable
  – Not suitable for high-speed or long-range scenarios
• IMUs & GPS
  – Provide vehicle position, orientation, and acceleration
  – Crucial for dead reckoning and localization in dynamic environments

“There is no silver bullet in sensing. Each sensor has strengths and blind spots. True perception comes from redundancy and data fusion.”
— Dr. Alex Grbic, CTO, AEye Lidar Systems

Sensor Fusion: Making Sense of the Senses

Sensor fusion is the process of combining data from multiple sources to create a single, accurate picture of the world.

There are three levels of fusion:

• Low-Level (Raw Data Fusion): Merges sensor outputs before object detection
• Mid-Level (Feature-Level Fusion): Combines features like edges, patterns, velocities
• High-Level (Decision-Level Fusion): Merges classified objects and decisions from each sensor modality

The goal: eliminate uncertainty and contradictions between sensors. A well-designed fusion system answers not just what is in front of the vehicle—but also how certain we are.

“Sensor fusion is essential to provide the redundancy and confidence needed for functional safety, especially at SAE Levels 3–5.”
— White Paper by Mobileye & Intel, 2022

Real-World Fusion in Action: Use Cases by Industry

Public Transport:
Autonomous shuttles rely heavily on camera-lidar fusion to navigate intersections, detect passengers, and obey traffic signals—even in urban chaos.

Ports & Logistics:
Low-speed cargo movers at ports use radar-lidar fusion for collision avoidance, while GPS and IMUs provide centimeter-level docking accuracy in container handling.

Mining:
Dust, debris, and rock surfaces require radar-dominant fusion systems, supported by thermal cameras in low-light tunnels.

Defense & Convoys:
Teleoperated vehicles in hostile terrain use multi-layered sensor arrays, often duplicated on separate computation stacks for fail-operational capability.

From Data to Decision: The Latency-Safety Tradeoff

Sensing alone is not enough. In safety-critical systems, latency is the enemy. Autonomous vehicles must go from “perceived object” to “safe maneuver” in milliseconds.

This is where Drive-by-Wire technology intersects perception. Once a fused object is confirmed, the vehicle must steer, brake, or accelerate instantly—with the reliability of aerospace-grade systems. That’s why redundant, fail-operational actuation architectures like NX NextMotion are so vital in the chain.

Looking Ahead: AI-Powered Perception and Edge Computing

Next-generation vehicles will shift more perception logic to edge AI systems—onboard computers capable of processing raw sensor data without needing cloud latency.

But that also means increased demand for thermal management, real-time computing, and even cybersecurity in the sensor and fusion layers—areas often overlooked in early-stage autonomy pilots.

Conclusion: True Perception Is Redundant, Robust, and Real-Time

The sensor suite is the foundation of trust in autonomous vehicles. But true safety doesn't lie in any one technology—it lies in how these systems work together, validate each other, and ensure safe decisions in dynamic environments.

In the next article, we’ll move from "seeing" to "thinking" – exploring how software stacks plan, decide, and act in autonomous vehicles.

References

• BMDV (2024). Handbuch: Autonomes Fahren im Öffentlichen Verkehr.
• Mobileye (2022). Safety Architecture for Self-Driving Vehicles.
• UNECE R155 (2021). Cybersecurity and Cybersecurity Management System.
• Arnold NextG (2025). Fail-Operational Drive-by-Wire Systems White Paper.
• Dr. Alex Grbic, CTO AEye, Interview with The Robot Report, 2023.
• Intel & Mobileye Whitepaper Series, 2022.