Experience our Perception Development Kit

A demonstration and evaluation kit for the Mapless Autonomy Platform – witness real-world autonomy on your vehicle under your operating condition.
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Achieve super-human performance safely by mixing classic and deep-learning algorithms.


Prepare sensor data for feature detection with compute-efficient algorithms.

Autonomous vehicles are equipped with a diverse sensor set – cameras, LIDARs, RADARs, and others. They produce a lot of data which has to be prepared for the various perception tasks. This includes appropriate filtering, merging , and time synchronization of sensor data. This data is usually represented as 3D point clouds or images.

It’s important to ensure minimum latency through the full perception pipeline. Our algorithms serve as building blocks with standardized interfaces for your customized pipeline – ensuring minimum latency, real-time capabilities and reliable operation.

Understand the world around the vehicle – detect vehicles, pedestrians, and everything else.

Sensors are the eyes and ears of an autonomous vehicle. The collected data is used to identify other traffic participants, like vehicles or pedestrians, and objects which might block the way, such as boxes or cargo lost earlier on the road.

The driveblocks stack provides a variety of implementations for object detection using classical algorithms, such as pointcloud clustering, and data-driven approaches, such as image- or point-cloud based neural networks. The implementations are complemented by a set of tools to adjust them towards your application. This allows you to evaluate the performance of the current setup, improve the pre-trained weights of the neural networks or analyze the execution times.

Where can autonomous vehicles move?

An autonomous vehicle has to understand where to drive. Lane markings guide the way on the public road, pylons mark the allowed driving corridors in construction sites, and the driving surface in off-road application is highlighted by the color difference of the condensed material.

The driveblocks perception algorithms are completed by a set of neural networks ready to take on the drivable space detection task. They are accompanied by various customization options and allow you to add data, specific to your application and sensor setup to enhance performance.

Leverage the power of various sensing modalities


Fuse drivable space information and operate without high-definition maps.

Driverless vehicles use several sensors of different types and at varying positions. The generated detections have to be combined to build a reliable, safe and accurate representation of the world around it.

Utilize our sensor fusion for driving corridor identification to overcome the limitations provided by high-definition maps. Our technology operates reliably in edge-cases, such as construction sites or off-road applications. We leverage a unique probabilistic fusion technique to combine up to 8 data sources (e.g. driveable space detections in image or pointcloud domain) in real-time.

Combine IMU, wheelspeeds, and visual odometry data for best-in-class motion estimation.

Precise knowledge of the vehicles speed and position is essential for safe driving behavior. We leverage the strengths of various information sources, such as IMU, wheelspeeds, and visual odometry, to obtain the best possible estimate. In addition, the driveblocks state estimator enables time synchronization for individual sensor sources and fault tolerant fusion which is key to achieve your safety targets.

Inside the
Mapless Autonomy Platform.

We accelerate your journey to fully autonomous driving with ready-to-deploy software modules for perception and sensor-fusion.
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What people say about driveblocks

The highly modular approach in conjunction with the utilization of open-source technologies makes the driveblocks software platform an ideal framework for autonomous driving in the commercial vehicle sector
Markus Lienkamp | Professor of Automotive Technology at TUM
The driveblocks software platform enables OEMs to automate their vehicles in weeks instead of years and allows them to achieve certification targets cost efficiently
Christian Wagner | Investor and Founder in-tech GmbH