One Chip.
Complete Autonomy.
Multi-modal sensor fusion, AI inference, and real-time control—all in a single SoC. Built for drones, robots, and autonomous fleets.
One Chip.
Simplifying Autonomous Vehicle Intelligence
The complexity of autonomous vehicle systems—with separate units for perception, decision-making, and control—creates integration challenges, increases power consumption, and introduces latency. SteadyBeat’s Autonomous Vehicles AI Module consolidates multi-modal AI inference and vehicle control into a single, power-efficient Edge AI SoC, enabling real-time decision-making with simplified system architecture.
Challenges in Autonomous Vehicle Development
Understanding the technical and operational challenges facing autonomous vehicle manufacturers
Fragmented System Architecture
Traditional autonomous vehicle systems require separate processing units for perception, decision-making, and control, leading to complex integration, high power consumption, and increased latency.
Impact
- Higher system complexity and cost
- Increased power consumption
- Integration challenges and delays
Real-Time Processing Demands
Autonomous vehicles must process massive amounts of multi-modal sensor data (camera, LiDAR, radar, acoustic) in real-time to make split-second decisions.
Impact
- Safety-critical latency issues
- Computational bottlenecks
- Limited scalability
Power and Thermal Constraints
High-performance computing platforms consume significant power and generate heat, challenging for battery-powered autonomous vehicles with limited cooling capacity.
Impact
- Reduced vehicle range
- Thermal management complexity
- Higher operational costs
Unified Edge AI Architecture
SteadyBeat’s Autonomous Vehicles AI Module integrates multi-modal sensor processing, AI inference, and vehicle control into a single, power-efficient SoC designed specifically for autonomous vehicle applications.
Advanced ANC
Real-time analysis and adaptive noise cancellation algorithms
Multi-Modal Fusion
Acoustic, visual, physiological, and environmental sensors
Edge AI Processing
On-chip inference with <10ms latency, privacy-preserving
How It Works
Key Benefits
Unified AI Inference & Control
Integrated Edge AI SoC combines multi-modal sensor fusion, AI inference, and vehicle control in a single chip, eliminating the need for multiple processing units and simplifying system architecture.
Technical Achievement
Single-chip solution with integrated NPU, DSP, and control interfaces
Ultra-Low Power Consumption
Optimized edge computing architecture achieves high-performance AI processing while consuming less than 10W, extending vehicle range and reducing thermal management requirements.
Technical Achievement
Advanced power management with dynamic voltage/frequency scaling
Real-Time Decision Making
Sub-10ms latency from sensor input to control output ensures safety-critical decisions are made instantly, even in complex urban environments.
Technical Achievement
Hardware-accelerated AI inference with deterministic real-time OS
Production-Ready Modularity
Flexible, modular design supports various autonomous vehicle platforms—from delivery robots to passenger vehicles—accelerating time-to-market and reducing development costs.
Technical Achievement
Standardized interfaces with scalable processing capabilities
Real-World Applications
See how our unified AI module powers different autonomous vehicle platforms
Autonomous Delivery Robots
Situation
Last-mile delivery robots navigate crowded sidewalks and crosswalks, requiring real-time obstacle detection, path planning, and decision-making in unpredictable urban environments.
Solution
- Unified AI module processes camera, LiDAR, and acoustic data in real-time
- Instant navigation decisions with multi-modal obstacle detection
- Minimal power consumption extends operational hours per charge
Outcome
Extended operational hours per charge, safer navigation in complex environments, and reduced system cost through integrated architecture.
Autonomous Shuttles
Situation
Low-speed autonomous shuttles in campuses, airports, and business parks require reliable perception and decision-making while maintaining passenger comfort and safety.
Solution
- Multi-modal sensor fusion combines visual, acoustic, and environmental data
- Edge AI processing ensures real-time response for comprehensive situational awareness
- Redundant perception systems enhance safety and passenger trust
Outcome
Enhanced safety through redundant perception, smooth ride quality, and predictable behavior that builds passenger trust.
Agricultural Autonomous Vehicles
Situation
Autonomous tractors and harvesters operate in challenging outdoor environments with varying lighting, weather, and terrain conditions.
Solution
- Robust multi-modal AI processing adapts to diverse environmental conditions
- Acoustic sensing detects mechanical issues and environmental hazards proactively
- All-weather operation with adaptive algorithms for varying lighting and terrain
Outcome
Reliable operation in all conditions, proactive maintenance through acoustic monitoring, and improved operational efficiency.
Ready to Accelerate Your Autonomous Vehicle Development?
Discover how SteadyBeat’s unified AI module can simplify your system architecture and accelerate time-to-market.