AUTOMOTIVE EXTERIOR

In this use case, we test different scenarios with our Ultrasonic 3D Echolocation Sensor:

  • Driving in an outside environment
  • Detection of curbstones
  • Driving in a parking garage
  • Detection of parking spaces
  • Human and pole recognition

To compare the data we use an additional 2D LiDAR on the car (red data points in the video).

Keep in mind that our automotive sensor is in early prototype status.

SENSOR SYSTEM USED:

ULTRASONIC 3D ECHOLOCATION SENSOR

Ultrasonic 3D Echolocation Sensors work by combining the time-of-flight principle of conventional ultrasonic sensors with triangulation and advanced signal processing algorithms. Therefore the sensor can locate objects in three-dimensional space and distinguish between multiple objects in a single scan.

Learn more about the Technology

Automotive Exterior Use Case

Sensors are currently used to help drivers steer more safely and guide them into a parking spot via sound and light signals. To advance in areas like autonomous driving, mapping, and collision avoidance, sensors now need to detect more complex environmental scenarios, like steering a car through a construction site or reliably detecting people in a crowded parking area. As every ride starts and ends in a parking position, involving some kind of parking maneuver, it is crucial to safely perceive a car’s immediate environment!

Toposens-3D-ultrasonic-sensor

For perceiving the close-range environment, we mounted eight of our Ultrasonic 3D Echolocation Sensors on a car. Four of them on the front side and four of them on the backside. This enabled us an all-around detection of objects within 6 – 7 meters of the vehicle. The sensors were connected via a CAN bus to a PC (or Laptop) running ROS, which was used to record and visualize the raw data points in a global coordinate system.

Furthermore, we attach a 2D LiDAR system to the vehicle, which we used for the reference data collection.

With the CAN interface, we are able to control the entire sensor communication. A virtual image of the sensor positions on the vehicle body is used to fuse the individual point clouds of the sensors into a common coordinate system.

Through the echolocation ability of our sensors, we are able to distinguish between deep-lying objects such as curbs and other obstacles like vehicles and walls. Furthermore, it is possible to hide unwanted object reflections, such as those generated by various road surfaces (e.g. cobblestones).

It is also possible to detect objects which are located higher up, such as air conditioning units or architecture-related extrusions in parking garages.

 

Further development

In addition to the sensor performance, we are focusing on data security and the integration of sensors in vehicles without influencing the vehicle design. We are also working on software modules that aim to classify objects found in the point cloud data, e.g. whether a parking space is free or not.

If you are interested in using our sensor system in one of your applications, or if you are interested in this automotive application, contact us!

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