A number of automobile manufacturers have announced plans to bring automated driving technology to the road in the near future. In addition to economic and social benefits, potentially improved safety performance is one of the key factors motivating automated driving. With high-quality sensors working in parallel, automated driving is likely to offer safety benefits, since the system observes the environment continuously in all directions, whereas human visual perception is directly limited by the field of view and can be indirectly limited by the complexity of a scene or the cognitive burden . Humans are also subject to lapses of attention, e.g. due to fatigue.

Assessing the difference in safety performance between automated and manual driving involves a det ailed analysis of this replacement. It is a more challenging task than assessment of “conventional” driver assistance systems, which usually address a more limited set of scenarios, and requires a comprehensive approach that includes the entire range of exposure of automated driving.

In general, safety assessment utilizes evidence from a variety of sources, beginning with retrospective accident analyses and including a range of prospective techniques, such as so -called naturalistic driving or field operation testing (FOT). This paper presents a technique used by BMW for assessing safety performance of highly automated driving functions using virtual experiments , including two design approaches:​ virtual scenario-based trials and virtual FOT.

The core technology in both designs is an agent-based Monte-Carlo simulation engine using our “stochastic cognitive model” (SCM) to describe human traffic participants, as well as sensor and functional models to describe agents with ADAS or automated driving. The paper will review key features of SCM developed to represent the behavior of virtual traffic participants (and their interactions) in real traffic. The simulation and models are parameterized base on different data sources like previous FOT -data, simulator studies, traffic data as well as accident data.

The virtual scenario-based trial design to test a virtual automated driving function (ADF) is illustrated here for two highway scenarios. Virtual “humans” drive according to a “stochastic cognitive model” developed by BMW. In an “obstacle in the lane” scenario, the virtual drivers encounter an obstacle that may appear (from their point of view) suddenly. Drivers are thus forced to decide on an action (braking, swerving, etc.) under severe time pressure, obvious collision risk, and often with inadequate time for observation of blind spots. In a “jam approach” scenario, the virtual “human” drivers enter a realistically simulated traffic jam front (e.g., position of maximum speed gradient can vary among lanes). In jam approach, typical perceptual limitations of human drivers can result in inadequate braking or counter-productive lane changes and ultimately in collisions. Target vehicles equipped with a virtual ADF achieve improved safety in both tested scenarios.