Forecast horizon for automated safety actions in automated driving systems

Abstract

Future Automated Driving Systems (ADS) will ultimately take over all driving responsibilities from the driver. This will as well include the overall safety goal of avoiding hazards on the road by executing automated safety actions (ASA). It is the purpose of this paper to address the general properties of the ASA. One property of particular interest is the forecast horizon (FH) that defines how much in advance a hazard has to be identified in order to ensure the timely execution of an ASA. For the estimation of the FH, we study the fault-tolerant time interval concept defined by the ISO 26262 and extend it for the use case of fail-operational ADS. We then perform a thorough study on all parameters contributing to the FH, assign typical values for each parameter for a running example, and formalize our work by a set of equations. The set of equations are then applied to two specific driving scenarios, and based on the running example values, the FH is estimated. We conclude our work with a summary of the estimated FH for each of the specific driving scenarios at different road conditions and the recommended road speed limits. Such a scientific way of deciding optimal bounds on the FH is essential to ensure the safety of the future autonomous vehicles and can be a major requirement for clearing the regulatory needs on certification.