Dynamic Risk Management (DRM)
Safe, comfortable and driver-accepted automated driving based on the prediction of possible road risks.
Less critical incidents, minimised number of accidents and higher passenger satisfaction.
Dynamic Risk Management (DRM) enables safe, comfortable and driver-acceptable automated driving based on the prediction of potential traffic risks. The approach is transferable, as DRM is an application of artificial intelligence (AI) that constantly monitors and combines data streams from different sources and makes decisions depending on the current situation.
Today's functions of autonomous driving systems are limited to assisting the driver or controlling the vehicle in simple, clearly defined situations, such as parking or driving on the highway. Responsibility still lies in the hands of the driver. The autonomous driving systems have not exceeded automation level 3 in series production.
EDI GmbH has developed an intelligent algorithm (EDI Dynamic Risc Management) that enables autonomous driving systems to manage various risks on the road as dynamically as experienced and responsible human drivers would.The AI can determine driving behavior that is perceived as appropriate for the driver, the passengers and other road users, in addition to purely evaluating the driving context in terms of safety.
The original application area for our DRM algorithm is autonomous driving. For the development of the DRM algorithm, over 100,000 critical road traffic incidents were evaluated from recorded data with imagery using machine learning. Each incident was then specified manually by traffic experts with over 100 different parameters and in some cases with up to 10 characteristics. Using the trained AI, further recorded journeys can now be automatically evaluated. The relevant parameters are extracted automatically.
Some of the parameters that have been weighted into different risk levels with the AI are driver behavior, speed history in different situations, existing infrastructure and intersection and road types. Another important aspect considered in the model of our DRM is the behavior of other road users: are they pedestrians, cyclists or other cars? The age of the pedestrians and whether they are drunk or not also plays a role.
Overall, our DRM covers a very large parameter space and the algorithm is correspondingly powerful: it can predict critical situations. Thus, "safety", the 3rd dimension of navigation, is implemented predictably. The algorithm is so robust that an assessment of the situation can be made even if not all parameters are available. The more information there is, the more accurate the statement is, of course.
The required data comes from different sources, such as the digital map of the navigation system and the camera system of the autonomous vehicle, which might perceive many cyclists who are in front of the vehicle for example. In addition, there is data from other sensors of the autonomous vehicle such as radar, ultrasound and lidar and further data from sensors that may be located in the public infrastructure and that can communicate with the autonomous vehicle.
The approach is transferable, which is why the areas of application for Dynamic Risk Management are not just self-driving vehicles. It is also used in our well-being barometer, which accompanies seniors in their daily lives and notifies relatives and caregivers when there are deviations in the daily routine. A large butcher's shop could also use the DRM to predict when demand for grilled sausages will be particularly high. Weather data and the occurrence of major events play a role here. Thanks to our DRM, the butcher no longer has to rely solely on his gut feeling. He then also has reliable support from the AI. This example also illustrates the concrete influence of AI on entrepreneurial decisions.
If you are thinking about using AI in your company, please do not hesitate to contact us. We look forward to hearing from you and discussing your specific question and first steps!