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ADAS Signal Processing

The first stage in the autonomous vehicle system is the perception stage.
The environment both near and further away is combined with localization data.

ADAS Signal Processing

ADAS Signal Processing

The autonomous vehicle must be able to work under all types of road, weather and light conditions. This includes nighttime, low light conditions, fog, rain, snow, black ice etc.

To be able to properly plan the right course of action the vehicle must perceive its location and itsenvironment. The environmental sensing is derived from inputs from its many sensors - cameras, radar, lidar, ultrasonic, GNSS etc. The data from the sensors is then either pre-processed locally or sent as raw data to the tracking system. The fused data for the tracked object(s) will then be handled by the next stage of the system.

VSORA's algorithm agnostic architecture makes it easy to implement the algorithms using high-level (Matlab-like) language.

Highlights

High processing power

High processing power eliminates need for specific coprocessors and hardware accelerators ensuring greater flexibility.

Customizable hardware

Customizable hardware with a configurable number of ALUs per core and unlimited number of cores.

User defined quantization

Floating point precision can be user specified to optimize the system.

Optimal algorithm mapping

Optimally maps algorithms, including non-parallel algorithms.

Automatic interconnect handling

Automatic handling of the interconnect between the different cores.

Low power

Lower energy consumption / power constraints.

High efficiency

  • Signals handled in hardware.
  • Signal memory bandwidth scales with MPU processing power.
  • Multi-instructions per cycle / rich set of instructions.

Handles higher processing requirements

Higher processing power to handle MiMo, beamforming and carrier aggregation requirements.

Futureproof

Futureproof should standards evolve.

Easy system programming

Compilation platform separates codes running in the different cores.

Platform independent design flow

High-level, platform independent design flow (C++/Matlab-like).

Algorithms mapped to different cores

Easy mapping of algorithms to different cores.

Optimized silicon area

Silicon area optimized to required processing power.

Combining Track Lists

Track-to-Track Fusion

Sensors observing the vehicle surroundings pre-process the data using tracking algorithms, extracting the information required. Only the extracted information is passed on to the next stage for the environmental perception. This means that the information from the various sensors are combined as lists of tracked objects. This is commonly called "track-to-track" fusion.

Combining Track Lists

Central Fusion

If the sensors pre-process the data using predetermined tracking algorithms some information will be lost, as only the list of tracked object will be passed on. In some cases having the ability to combine the raw data directly from the sensors may allow for a more comprehensive and complete understanding of the environment, which then will make the next stage involving (mission, behavioural and motion) planning and control easier. Central fusion requires higher computational power though.