Failure Prediction for Autonomous Driving

Abstract

The primary focus of autonomous driving research is to improve driving accuracy. While great progress has been made, state-of-the-art algorithms still fail at times. Such failures may have catastrophic consequences. It therefore is important that automated cars foresee problems ahead as early as possible. This is also of paramount importance if the driver will be asked to take over. We conjecture that failures do not occur randomly. For instance, driving models may fail more likely at places with heavy traffic, at complex intersections, and/or under adverse weather/illumination conditions. This work presents a method to learn to predict the occurrence of these failures, i.e. to assess how difficult a scene is to a given driving model and to possibly give the human driver an early headsup. A camerabased driving model is developed and trained over real driving datasets. The discrepancies between the model's predictions and the human 'ground-truth' maneuvers were then recorded, to yield the 'failure' scores. Experimental results show that the failure score can indeed be learned and predicted. Thus, our prediction method is able to improve the overall safety of an automated driving model by alerting the human driver timely, leading to better human-vehicle collaborative driving.

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

“…Currently, there is no unified performance metric for assessing the driveability of a scene, because driveability assessment involves many tasks in perception and behavior analysis, and the performance metric for each task is tightly related to the underlying model used. In this section, we introduce the most relevant metrics established in existing research for scene driveability evaluation [4] and risk assessment [70], and present the design methodology underlying these metrics, with the purpose of encouraging the proposal of novel metrics for driveability assessment. While risk assessment metrics are well established and accepted in studies on ADAS, a metric for scene driveability has only been proposed recently and it is aimed at end-to-end driving policy learning.…”

“…In [4], scene driveability is defined by how easy a scene is for an autonomous car to navigate and a scene driveability score is used to measure how likely the car will fail. An endto-end approach is used to calculate this score.…”

“…For example, with ADAS and current Level 2 or 3 autonomy, a scene can be simply defined as driveable if the car can operate safely in autonomous mode. When a non-driveable scene is detected, the autonomous car can hand over control to the human driver in a timely manner [4]. However, in the long term where Level 4 or 5 autonomy is targeted, it is not possible to hand over control to the driver.…”

“…Related usages of driveability include "driveability map" which is a map divided into cells categorized as driveable or non-driveable for motion planning [11]- [14], and "object driveability" which determines if an object can be driven over without causing damage to the vehicle [15]. The concept that is most similar to that considered in this paper is "scene driveability" proposed in two recent studies [4], [16]. Scene driveability measures how easy a scene is for an autonomous car to make full decisions on steering angles and accelerations or to perform a specific task such as lane changing.…”

Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

“…It analyzes the input and predict the output reliability of the vision system. [13] has proposed the concept of scene drivability. It predicts the feasibility of driving scene for a driving method.…”