Multi-Sensor Perception with Versal, AI Engine and Vitis AI NPU
Advanced Driver Assistance Systems (ADAS) are evolving from single-sensor perception toward multi-sensor intelligence, combining data from cameras, LiDAR, RADAR and other vehicle sensors to build a comprehensive understanding of the road environment.
LogicTronix has developed an ADAS Sensor Fusion AI/ML network stack and repository targeting heterogeneous acceleration platforms, including AMD Versal Adaptive SoC, AI Engine and Vitis AI NPU. The platform brings together neural networks, AI processing pipelines and sensor-fusion logic to enable real-time perception, tracking and scene understanding.
Our development includes more than 15 neural networks and AI/ML models designed for different stages of the ADAS perception pipeline. These networks operate on individual sensor data as well as combinations of Camera, LiDAR and RADAR data.
A Multi-Sensor AI/ML Repository for ADAS
The LogicTronix ADAS AI/ML repository is designed around a modular architecture where different neural networks and AI processing blocks can be deployed depending on the target vehicle application.
The stack covers:
- Camera-based perception
- LiDAR point-cloud perception
- RADAR-based perception
- Camera + LiDAR fusion
- Multi-camera perception
- Bird’s-Eye View (BEV) perception
- Object tracking
- Vehicle behavior analysis
- Object trajectory prediction
- Road and lane understanding
The objective is not only to run neural networks, but to create a complete hardware-accelerated perception pipeline capable of processing heterogeneous sensor data with low latency.
Key ADAS AI/ML Networks
1. Road and Lane Segmentation
Segmentation networks are used to understand the structure of the road environment at a pixel level.
LogicTronix has developed segmentation pipelines for identifying and separating:
- Road surfaces
- Lane boundaries
- Road markings
- Drivable areas
- Road structures
- Other relevant scene regions
The segmentation output can be used by downstream ADAS functions such as lane detection, path planning and road-scene understanding.
2. Camera-Based Object Detection
Object detection networks provide real-time detection of objects from different camera views.
The detection pipeline can identify and classify objects such as:
- Vehicles
- Pedestrians
- Cyclists
- Traffic signs
- Traffic lights
- Road obstacles
- Other road users
The architecture can support multiple camera perspectives, enabling perception across front, rear and surround-view cameras.
Camera detection outputs can then be combined with other sensor information to improve the overall perception capability.
3. LiDAR Point-Cloud AI
LiDAR provides three-dimensional information about the surrounding environment.
LogicTronix’s LiDAR AI/ML pipeline processes point-cloud data to determine objects and environmental structures, including:
- Vehicles
- Pedestrians
- Traffic signs
- Traffic lights
- Road surfaces
- Road boundaries
- Surrounding structures
- Obstacles
The LiDAR processing pipeline is designed to transform raw point-cloud data into meaningful perception information for downstream ADAS applications.
This enables the system to move from raw 3D sensor data to semantic understanding of the environment.
4. LiDAR BEV with Camera Fusion
Bird’s-Eye View (BEV) perception provides a top-down representation of the vehicle’s surroundings.
LogicTronix’s BEV pipeline combines LiDAR information with camera perception to generate a richer representation of the surrounding environment.
The fusion pipeline can combine:
Camera → 2D perception
LiDAR → 3D spatial information
Fusion → BEV representation
This approach enables improved spatial understanding of objects around the vehicle and provides a useful representation for downstream ADAS and autonomous-driving algorithms.
5. LiDAR AI Logic for Object Tracking and Trajectory Prediction
Object detection is only one part of an ADAS perception system. A vehicle must also understand how detected objects are moving.
LogicTronix has developed AI logic around LiDAR perception for:
- Object tracking
- Object association
- Movement estimation
- Velocity estimation
- Trajectory analysis
- Object path prediction
The system can maintain information about detected objects across successive frames and estimate their future movement.
This is particularly important for applications involving:
- Vehicle tracking
- Pedestrian tracking
- Collision avoidance
- Path prediction
- Autonomous navigation
6. Camera AI Logic for Vehicle Behavior Analysis
Camera perception can provide additional information about vehicle movement and behavior.
LogicTronix’s camera AI logic can be used for vehicle tracking, identification and behavior analysis, enabling the system to analyze movement patterns across successive frames.
Potential outputs include:
- Vehicle tracking
- Vehicle identification
- Lane movement
- Direction of movement
- Relative behavior
- Traffic behavior
- Abnormal or unexpected movement patterns
Combining temporal information with object detection allows the perception system to move beyond simply answering “What is the object?” toward understanding “What is the object doing?”
Hardware Acceleration with AMD Versal
A key aspect of the LogicTronix ADAS platform is its ability to map different AI workloads onto the heterogeneous compute resources available in AMD Versal Adaptive SoC platforms.
A typical architecture can distribute workloads across:
Vitis AI NPU
For highly optimized neural-network inference and supported AI/ML workloads.
AI Engine
For high-throughput signal processing, feature extraction, tensor operations and custom AI processing.
Programmable Logic
For deterministic, low-latency data movement, preprocessing, sensor interfaces, custom accelerators and application-specific logic.
Processing System
For system management, control, orchestration, communication and higher-level application software.
This heterogeneous architecture allows the ADAS pipeline to be optimized according to the characteristics of each workload rather than executing the entire system on a single processor.
From Sensor Data to ADAS Intelligence
The overall LogicTronix architecture can be viewed as a complete processing chain:
Sensors → Data Acquisition → Preprocessing → Neural Networks → Sensor Fusion → Tracking → Prediction → ADAS Intelligence
For example:
Camera
→ Image preprocessing
→ Object detection
→ Segmentation
→ Vehicle tracking
LiDAR
→ Point-cloud preprocessing
→ 3D object detection
→ BEV generation
→ Object tracking
→ Trajectory prediction
Camera + LiDAR
→ Feature/object fusion
→ BEV perception
→ Improved object localization
→ Scene understanding
The result is a modular perception stack that can be adapted to different ADAS architectures and vehicle platforms.
Why Heterogeneous AI Acceleration Matters
ADAS applications place demanding requirements on both performance and latency.
A perception system may need to process multiple high-resolution cameras, large LiDAR point clouds and additional sensor streams simultaneously while maintaining deterministic response times.
A heterogeneous architecture enables different workloads to be mapped to the most appropriate compute engine.
For example:
| Workload | Potential Accelerator |
|---|---|
| Neural-network inference | Vitis AI NPU |
| Custom AI kernels | AI Engine |
| Signal processing | AI Engine |
| Image/point-cloud preprocessing | PL / AI Engine |
| Sensor data movement | PL |
| Custom accelerators | PL |
| System control | Processing System |
| Sensor fusion | AI Engine / PL / NPU |
This approach enables developers to optimize the complete pipeline rather than optimizing an individual neural network in isolation.
LogicTronix ADAS AI/ML Repository
The growing LogicTronix AI/ML repository provides a foundation for developing and evaluating different ADAS perception solutions.
The repository includes neural networks and AI logic targeting:
Camera AI
- Object detection
- Segmentation
- Tracking
- Vehicle behavior analysis
LiDAR AI
- Point-cloud object detection
- Road/environment understanding
- BEV perception
- Object tracking
- Trajectory prediction
Sensor Fusion
- Camera + LiDAR fusion
- Multi-sensor perception
- BEV-based fusion
- Object-level and feature-level processing
Hardware Acceleration
- Versal AI Engine
- Vitis AI NPU
- Programmable Logic accelerators
- Heterogeneous processing pipelines
Towards Physical AI for Automotive Systems
The next generation of ADAS is moving toward Physical AI, where AI systems continuously perceive, understand and predict the physical environment.
This requires more than a collection of neural networks.
It requires an integrated system capable of:
Perception → Fusion → Tracking → Prediction → Decision Support
LogicTronix is building this capability through its ADAS AI/ML network repository and hardware acceleration expertise across FPGA, Versal Adaptive SoC, AI Engine and Vitis AI NPU technologies.
With more than 15 neural networks already developed for different ADAS perception tasks, LogicTronix continues to expand its AI/ML stack toward real-time, multi-sensor automotive perception and Physical AI applications.
LogicTronix ADAS AI/ML Stack
Camera + LiDAR + RADAR
↓
Sensor Processing
↓
AI/ML Perception
↓
Object Detection + Segmentation + 3D Perception
↓
Sensor Fusion + BEV
↓
Object Tracking
↓
Trajectory & Behavior Prediction
↓
ADAS / Physical AI Intelligence
The combination of AI/ML expertise and hardware acceleration enables LogicTronix to develop application-specific perception pipelines optimized for embedded automotive systems.