Sensors & Compute Overview

Sensors and compute form the physical substrate of autonomy. They determine what an autonomous system can perceive, how quickly it can reason about the world, and how reliably it can execute decisions. Vehicle and robot behavior is ultimately bounded by sensing fidelity, compute throughput, thermal headroom, and the architecture that ties everything together.

This section focuses on the hardware capabilities that make autonomy stacks viable: the richness of perception, the latency of prediction and planning, and the robustness required for safety-critical operation across vehicles, mobile robots, and humanoids.

Camera System

Cameras are the primary perception modality for many autonomy stacks due to their resolution, range, cost structure, and overlap with human driving intuition.

Typical camera suite components include:

forward long-range cameras
surround cameras with wide fields of view
fisheye or ultra-wide cameras for near-field understanding
interior or cabin cameras
stereo or pseudo-stereo depth estimation pipelines

Key strengths are high information density, semantic richness, and attractive cost at scale. Challenges include sensitivity to weather and lighting, the compute load of multi-camera inference, and the need for robust temporal and spatial fusion. Modern implementations increasingly rely on end-to-end neural vision systems with learned depth and occupancy fields.

Radar System

Radar provides reliable measurement of range and relative velocity under diverse weather and lighting conditions.

Common radar types include:

short-range radar for blind spots and low-speed maneuvers
medium and long-range radar for highway autonomy
imaging or four-dimensional radar for higher-resolution point clouds

Radar is robust in rain, fog, dust, and night, and provides accurate velocity estimation. It is often used for collision avoidance, highway following, and as a redundancy layer in sensor fusion. The shift toward imaging radar increases its relevance in camera-first autonomy stacks.

LiDAR System

LiDAR provides direct three-dimensional spatial measurements and is commonly used in high-spec autonomy stacks or domain-constrained robots.

Typical LiDAR categories include:

spinning LiDAR with near-360 degree coverage
solid-state LiDAR for targeted fields of view
frequency-modulated continuous wave LiDAR with velocity channels

LiDAR offers high-resolution depth information and reliable three-dimensional occupancy structure, but can be limited by cost, power, contamination, and integration complexity. It is frequently seen in robotaxis and industrial robotics and less often in cost-sensitive consumer platforms.

Other Sensors

A range of supplementary sensors provide local context, redundancy, and state estimation.

Common examples include:

ultrasonic sensors for short-range detection in parking and docking
time-of-flight sensors for near-field depth sensing
inertial measurement units for high-rate acceleration and angular velocity
GNSS or GPS for absolute global position in open environments
wheel encoders and joint encoders for precise motion feedback
force and torque sensors for manipulation and balance in robotics

These sensors enhance robustness and provide critical state information that perception, planning, and control layers rely on.

Compute Platforms

Compute determines how quickly and how richly the autonomy stack can process sensor data and generate actions.

Typical compute architectures include:

high-performance automotive system-on-chips
AI accelerators and neural network specific cores
domain controllers that consolidate perception, planning, and control
redundant compute paths for safety-critical decision making
custom silicon for autonomy workloads

Important metrics include operations per second, latency budgets, memory bandwidth, power draw, and thermal constraints. The trend is away from distributed electronic control units toward centralized compute with integrated perception and planning pipelines.

Thermal and Power Architecture

Autonomy compute runs at high thermal density, and managing heat and power is essential for stable performance.

Key requirements include:

dedicated heat sinks, liquid cooling, or optimized airflow paths
battery and power conversion sizing for sustained compute loads
thermal monitoring and safe derating behavior
isolation from drivetrain and high-voltage thermal events

In robotics, actuator heat and tight energy budgets introduce additional constraints that must be considered alongside compute cooling.

Data Pipelines and Bandwidth Architecture

Sensor-heavy autonomy systems must move large volumes of data across the vehicle or robot in real time.

Typical data architecture elements include:

high-bandwidth backbone networks using automotive Ethernet
time-sensitive networking for deterministic latency
compression and preprocessing at the sensor edge
prioritization of safety-critical data flows

High-quality planning and control depend on predictable, low-latency data movement. Bottlenecks in the data path can negate gains in sensing or compute capability.

Redundancy and Failover Hardware

Autonomy requires predictable behavior under fault conditions, and the redundancy strategy directly affects system safety classification.

Common redundancy and failover techniques include:

sensor redundancy through overlapping modalities
redundant compute pathways with hot standby or shadow compute
independent power rails for critical systems
fallback actuators or independent braking layers

The redundancy model often determines whether a system can be certified for higher automation levels on roads or for specific industrial safety standards in facilities.