Technology from Highly Automated Driving to Improve Active Pedestrian Protection Systems

Paper #:
  • 2017-01-1409

Published:
  • 2017-03-28
Abstract:
Highly Automated Driving opens up new middle-term perspectives in mobility and is therefore currently one of the main goals in the development of future vehicles. The focus is the implementation of Highly Automated Driving functions for structured environments, such as on the motorway. To achieve this goal, the vehicles are equipped with additional technologies (redundant surround sensing, highly precision digital maps, driver monitoring, etc.). These technologies shouldn’t only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving, to increase the road safety. Two potential uses will be discussed in this paper. 1) In the first approach we investigated the usage of machine learning to develop a braking strategy for AEB systems. Future vehicles could be able to record detailed information about an accident, which could include information about involved traffic participants, the driver behavior and road geometry. If a large amount of critical situations recorded by vehicles are available, then it is conceivable to use this data to learn the safety strategy and evaluate the system behavior on real world data. Unfortunately no recorded real world data is available, so we used for your investigation generated traffic scenarios from the effectiveness analysis for active safety systems. 2) For active systems it is important to know, if the driver is able to solve the situation by himself or an intervention from the vehicle is necessary. Driver behaviour is difficult to model. It is not feasible to compute every possible future state of the driver. Therefore, based on the work from Eidehall, a stochastic approach is implemented to calculate how long a driver is able to drive collision free, based on information about other traffic participants and highly accurate digital maps. To fulfil the timing requirements for further investigations in the simulation environment and the test vehicle the approach is implemented on a GPU. Both approaches are investigated for pedestrian AEB systems and results will be discussed in the paper.
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