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.
Highlights
About 90% of road accident fatalities are attributed to human errors such as distraction, fatigue, violation of traffic rules, and poor judgements (Treat et al, 1979; Katrakazas, 2017; Collet and Musicant, 2019; Wood et al, 2019)
Safety regulatory authorities are trying to formulate suitable performance metrics to quantify the safety of Automated Driving Systems (ADS)
It is highly appropriate to review the literature on metrics used to quantify the performance of ADS
Summary
About 90% of road accident fatalities are attributed to human errors such as distraction, fatigue, violation of traffic rules, and poor judgements (Treat et al, 1979; Katrakazas, 2017; Collet and Musicant, 2019; Wood et al, 2019). Successful execution of driving tasks by human drivers depends on 1) knowing the current state of self (such as location, speed, acceleration, and steering angle), 2) perceiving the states of surrounding obstacles, 3) planning the future course of action ensuring safety, and 4) controlling the vehicle using steering wheel, throttle, and brakes. ADS can be considered to have four primary modules (Figure 1): 1) localization, 2) perception, 3) motion planning, and 4) vehicle control. The process involves deciding the EV’s future states (position, velocity, acceleration) in the dynamic traffic environment Humans make such decisions based on multiple parameters (see Human-likeness and ADS). The EV’s safe and efficient movement in the dynamic traffic is made possible by the motion planning module using the current and future states of surrounding obstacles.
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