Analysis of Functional Insufficiencies and Triggering Conditions to Improve the SOTIF of an MPC-based Trajectory Planner

TL;DR

This paper analyzes the safety of MPC-based trajectory planning in autonomous vehicles, focusing on identifying functional insufficiencies and triggering conditions to enhance SOTIF compliance.

Contribution

It introduces a novel approach for out-of-context SOTIF development, compiles key FIs and TCs, and proposes an optimized safety concept for MPC-based trajectory planners.

Findings

Identified critical FIs and TCs for MPC trajectory planning

Developed an out-of-context SOTIF development approach

Proposed an improved safety concept based on analysis

Abstract

Automated and autonomous driving has made a significant technological leap over the past decade. In this process, the complexity of algorithms used for vehicle control has grown significantly. Model Predictive Control (MPC) is a prominent example, which has gained enormous popularity and is now widely used for vehicle motion planning and control. However, safety concerns constrain its practical application, especially since traditional procedures of functional safety (FS), with its universal standard ISO26262, reach their limits. Concomitantly, the new aspect of safety-of-the-intended-function (SOTIF) has moved into the center of attention, whose standard, ISO21448, has only been released in 2022. Thus, experience with SOTIF is low and few case studies are available in industry and research. Hence this paper aims to make two main contributions: (1) an analysis of the SOTIF for a generic MPC-based trajectory planner and (2) an interpretation and concrete application of the generic procedures described in ISO21448 for determining functional insufficiencies (FIs) and triggering conditions (TCs). Particular novelties of the paper include an approach for the out-of-context development of SOTIF-related elements (SOTIF-EooC), a compilation of important FIs and TCs for a MPC-based trajectory planner, and an optimized safety concept based on the identified FIs and TCs for the MPC-based trajectory planner.