A Systematic Scenario Typology for Automated Vehicles Based on China-FOT 2018-01-0039

To promote the development of automated vehicles (AVs), large scale of field operational tests (FOTs) were carried out around the world. Applications of naturalistic driving data should base on correlative scenarios. However, most of the existing scenario typologies, aiming at advanced driving assistance system (ADAS) and extracting discontinuous fragments from driving process, are not suitable for AVs, which need to complete continuous driving tasks. In this paper, a systematic scenario-typology consisting of four layers (from top to bottom: trip, cluster, segment and process) was first proposed. A trip refers to the whole duration from starting at initial parking space to parking at final one. The basic units ‘Process’, during which the vehicle fulfils only one driving task, are classified into parking process, long-, middle- and short-time-driving-processes. A segment consists of two neighboring long-time-driving processes and a middle or/and short one between them. A cluster refers to a series of segments during which the vehicle drives on the same type of roads. Using this typology, a trip can be divided into continuous fragments at all the four layers, making the functional definition and test & evaluation possible. An application in functional definition was carried out as example. Naturalistic driving data from China-FOT were analyzed to obtain statistics in travel time and mileage. In shielded roads, high speed cruise and automatic lane change are in high demand. In open roads, automatic lane change and intersection passing are urgent demanded. Method and results from this paper fill the gap of studies on scenarios typology for AVs.