Abstract

Automated Driving Systems (ADSs) show great potential to improve our transport systems. Safety validation, before market launch, is challenging due to the large number of miles required to gather enough evidence for a proven in use argumentation. Hence there is ongoing research to find more effective ways of verifying and validating the safety of ADSs. It is crucial both for the design as well as the validation to have a good understanding of the environment of the ADS. A natural way of characterizing the external conditions is by modelling and analysing data from real traffic. Towards this end, we present a framework with the primary ultimate objective to completely model and quantify the statistically relevant actions that other vehicles conduct on motorways. Two categories of fundamental actions are identified by recognising that a vehicle can only move longitudinally and laterally. The fundamental actions are defined in detail to create a set that is collectively exhaustive and mutually exclusive. All physically possible combinatorial actions that can be constructed from the fundamental actions are presented. To increase the granularity of the modelling the combinatorial actions are proposed to be analysed as sequences. Further, multi-vehicle interactions, which capture correlations between actions from multiple vehicles, are discussed. The resulting modularity of the framework allows for performing statistical analysis at an arbitrary granularity to support the design of a performant ADS as well as creating applicable validation scenarios. The use of the framework is demonstrated by automatically identifying fundamental actions in field data. Identified trajectories of two types of actions are visualised and the distributions for one parameter characterising each action type are estimated.

BibTeX

@inproceedings{gyllenhammar2020defining,
  title        = {Defining Fundamental Vehicle Actions for the Development of Automated Driving Systems},
  author       = {Gyllenhammar, Magnus and Zand{\'e}n, Carl and T{\"o}rngren, Martin},
  booktitle    = {World Congress (WCX)},
  year         = {2020},
  organization = {SAE}
}