A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

Abstract

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

“…Assessment of the execution of a scenario is a critical step for scenario-based ADS testing. Some assessment metrics have been proposed in the literature [25,29,40,48], such as time-based metrics, distance-based metrics, and deceleration-based metrics. Recently, the quantitative semantics of signal temporal logic (STL) is applied to evaluate scenarios, which measure how far a scenario is from violating the specifications formulated in STL [14,47,48,57].…”

A review on machine learning-based models for lane-changing behavior prediction and recognition

“…A commonly known property of ACC is that it performs poorly with a non-balanced data set (López et al, 2013), while DR and precision provide a better evaluation for non-balanced data sets (Powers, 2011). The drawbacks of DR and precision are that DR does not consider FP values, while precision does not consider FN values (Sharath and Mehran, 2021). Another well-known metric is AUC, which shows the relationship between recall and false positive rate (FPR).…”

Intelligent Data Handling in Current and Next-Generation Automated Vehicle Development—A Review

“…The ability to verify the performance of the ADAS or Autonomous Driving (AD) system under development is crucial from both the development team and vehicle manufacturer perspectives. This is why, in every project, evaluation metrics called Key Performance Indicators (KPIs) are defined [56].…”