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Industry Solution
Transportation
Explore transportation FPGAs for sensor processing, software-defined vehicle gateways and control, with resources for adaptable automotive system design.
FPGA technology in transportation
Transportation systems are becoming increasingly connected, software-defined, and data-centric. In automotive applications, the shift toward Software-Defined Vehicles (SDVs) and zonal architectures is driving greater integration of sensors, networking, compute, and control functions. Similar trends are occurring across rail, bus, commercial vehicle, off-highway, and transportation infrastructure systems, where sensing, diagnostics, automation, and networked control are expanding system capabilities. As these platforms evolve, engineers must process and act on growing volumes of data close to the vehicle or infrastructure while meeting requirements for deterministic performance, power efficiency, safety, security, environmental resilience, and long product lifecycles. A common design challenge is connecting diverse sensors and controllers, processing data predictably, and maintaining the flexibility to support future algorithms, interfaces, and product variants.
The sections below illustrate how these technologies fit within the transportation data path, from sensor acquisition and preprocessing through gateway and networking functions, AI and application processing, control and diagnostics, and ultimately to vehicle, actuator, or infrastructure outputs. Explore the application cards and Get Started resources to evaluate solutions for your specific transportation design requirements.
The Challenges, and Why FPGA
The Challenge
Sensor data growth
More cameras, radar, and lidar demand fast synchronization, preprocessing, and data fusion.
Why FPGA
Efficient sensor fusion
FPGA DSP blocks handle sensor capture, synchronization, preprocessing, and fusion, delivering clean data to the vehicle's AI processor.
The Challenge
Architecture transitions
Zonal and centralized platforms need flexible interfaces and predictable data movement.
Why FPGA
Adaptable real-time paths
Programmable logic adapts interfaces and accelerates gateways, preprocessing, and control.
The Challenge
Driver and occupant safety
Cabin systems must detect unsafe behavior and monitor occupant safety and comfort.
Why FPGA
Sensor aggregation for cabin monitoring
FPGAs enable fast driver monitoring, occupant sensing, and AI-based voice recognition.
The Challenge
Battery and system health
Vehicles need timely fault detection and battery monitoring to improve reliability.
Why FPGA
Predictive diagnostics
Local AI detects potential failures and monitors battery health, charge, and cell balancing.
Why Altera
Adaptable I/O and processing
Connect diverse sensors and networks with programmable interfaces, real-time preprocessing, and gateway and control functions.
Flexible system partitioning
Partition AI, data movement, and deterministic processing across processors and FPGA logic to match transportation workloads.
Development and reuse
Build with Altera tools, IP, reference designs, and development kits, and reuse design elements across transportation platforms.
Lifecycle flexibility
Adapt algorithms, interfaces, and product variants with reprogrammable FPGAs to support evolving needs over long service lifecycles.
Aerospace and Defense Applications
Software-Defined Vehicle (SDV)
Connect adaptable I/O, local control, and monitoring to evolving vehicle architectures with Altera programmable logic.
Advanced Driver-Assistance System (ADAS)
Use Cyclone® and MAX® FPGA technology for selected ADAS sensor-edge control, monitoring, and data preparation.
Electric Vehicle (EV) Powertrain
Use programmable logic for selected EV battery monitoring, power conversion, and motor-control functions.
Get Started
Common Questions
An FPGA can adapt interfaces, aggregate sensor traffic and implement supported deterministic data paths around vehicle processors. The processor typically manages software workloads; the architecture must define bandwidth, timing and qualification requirements for each function.
Move preprocessing when supported streaming operations, interface diversity or timing requirements justify it. Keep suitable application and AI workloads on the processor, and comparesystem-level latency, power and device qualification for the vehicle platform.
Define a common timing reference, timestamp near acquisition and track each interface's delay and clock quality. Verify alignment under operating conditions so fusion uses measurement time rather than asynchronous arrival order.
Check the exact device's qualification, temperature, reliability, lifecycle and supported documentation against the application requirements. A transportation solution page or development kit alone does not establish suitability for every operating environment.
Monitor interface errors, sequence counters, buffer occupancy and processor-transfer backpressure under simultaneous peak streams. Locate the saturated stage and verify timing before changing sensor-fusion algorithms.