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Transportation

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.

Transportation system challenges

Architecture transition

Automotive platforms are moving toward domain, zonal, and centralized compute, which increases the need for aggregation, gateway functions, deterministic data movement, and software-defined interfaces.

Sensor and data growth

Cameras and other sensors create streams that may require synchronization, preprocessing, format conversion, buffering, and transfer to an application processor or accelerator.

Safety, security, and lifecycle

Automotive, rail, bus, commercial, and infrastructure products operate in demanding environments and may require documented safety processes, secure configuration or updates, fault monitoring, environmental robustness, and long support periods.

Why FPGAs

Sensor processing and fusion

In order to make vehicles safer and easier to operate, the automotive industry is seeing a dramatic proliferation of cameras and other types of sensors, such as LiDAR, radar, and motion sensors around the vehicle and within the cockpit. Altera FPGAs and SoCs have specialized, AI-enabled Digital Signal Processors (DSPs), embedded throughout their logic fabric, which can be used to perform the demanding matrix multiplication tasks required by AI. These AI-enabled DSPs allow for very fast and efficient processing and fusion of sensor data so that it can be consumed efficiently by the central brain of the car, making the vehicle smarter, and safer.

Driver and occupant monitoring and voice controls

AI-enabled FPGAs can be used by camera systems and sensors within the cabin of the vehicle to identify drivers and occupants and to monitor driver behavior as well as occupant safety and comfort. AI-driven analysis of driving behavior can be used to alert the driver of potentially hazardous driving behavior, predict and prevent potential mishaps, and even allow the vehicle to take preventative actions to avoid a collision. In addition, AI can be used to implement very fast and highly accurate AI-based voice recognition algorithms to allow drivers and passengers to safely and easily interact with the vehicle.

Predictive maintenance, diagnostics, and battery management

FPGAs facilitate localized AI processing, allowing continuous monitoring of sensors, electrical and mechanical systems within the vehicle to predict potential failures before they occur and alert the vehicle owner of the need for preventative maintenance or repairs. They can also do localized monitoring of battery state-of-health and state-of-charge to perform advanced management and load balancing of battery cells to extend battery life and reduce the need for battery maintenance or replacement. AI capability within the FPGA is essential for time-sensitive sensing and diagnoses where fast, accurate insights can lead to increased reliability and extended life of the vehicle and its battery.

Why Altera

Transportation designers evaluate platforms against long development cycles, changing interfaces, data movement, timing, power, environmental, safety, security, and supply requirements. Altera’s programmable hardware, IP, software, and development resources can be considered where an architecture needs adaptable real-time processing around processors, accelerators, sensors, networks, or power-control functions. The selected device and implementation must be evaluated against the target qualification and system requirements.

Adaptable I/O and processing

For sensor aggregation, protocol adaptation, preprocessing, gateway, video, or control functions.

Heterogeneous system partitioning

For dividing orchestration, AI, data movement, and deterministic paths across processors and programmable logic.

Development and reuse

For public tools, IP, reference designs, and kits.

Lifecycle planning

For managing product variants, changing algorithms, and long service expectations.

Get Started

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High-Speed FPGA-based 800-volt eFuse

Improve high-voltage industrial and transportation system reliability and lower operating costs with a reprogrammable FPGA-based solution.

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