Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips

Abstract

The commercial adoption of Autonomous Vehicles (AVs) and the positive impact they are expected to have on traffic safety depends on appropriate insurance products due to the high potential losses. A significant proportion of these losses are expected to occur from the out-of-distribution risks which arise from situations outside the AV’s training experience. Traditional vehicle insurance products (for human-driven vehicles) rely on large data sets of drivers’ background and historical incidents. However, the lack of such datasets for AVs makes it imperative to exploit the ability to deploy AVs in simulated environments. In this paper, the data collected by deploying Autonomous Driving Systems (ADSs) in simulated environments is used to develop models to answer two questions: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how risky a road section is for an AV to drive?</i> and (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how does the risk profile vary with different (SAE levels) of ADSs?</i> A simulation pipeline was built on the CARLA (Car Learning to Act): an open-source simulator for autonomous driving research). The environment was specified using parameters such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weather, lighting, traffic density, traffic flow, no. of lanes</i> , etc. A metric - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">risk factor</i> was defined as a combination of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">harsh accelerations/braking, inverse Time to Collision</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inverse Time Headway</i> to capture the crashes and near crashes. To assess the difference between ADSs, two ADSs: OpenPilot (Level 2/3) and Pylot (Level 4) were implemented in the simulator. The results (from data and model predictions) show that the trends in the relation between the environment <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">features</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">risk factor</i> for an AV are similar to those observed for human drivers (e.g., risk increases with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">traffic flow</i> ). The models also showed that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">junctions</i> were a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">risk hot-spot</i> for both ADSs. The feature importance of the model revealed that the Level 2/3 ADS is more sensitive to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">no. of lanes</i> and the Level 4 ADS is sensitive to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">traffic flow</i> . Such differences in feature importance provide valuable insights into the risk characteristics of different ADSs. In the future, this base model will be extended to include other features (other than the environment), e.g., take over requests, and also address the deficiencies of the current simulation data in terms of insensitivity to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weather</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lighting</i> .