Development and validation of the Spanish hazard perception test

Objective: The aim of the current study is to develop and obtain validity evidence for a Hazard Perception test suitable for the Spanish driving population. To obtain validity evidence to support the use of the test, the effect of hazardous and quasi-hazardous situations on the participants’ Hazard Prediction is analysed and the pattern of results of drivers of different driving experience: learner, novice and expert drivers and re-offender vs. non-offender drivers, is compared. Potentially hazardous situations are those that develop without involving any real hazard (i.e., the driver didn’t actually have to decelerate or make any evasive manoeuvre to avoid a potential collision). The current study analysed multiple offender drivers attending compulsory re-education programmes as a result of reaching the maximum number of penalty points on their driving licence, due to repeated violations of traffic laws. Method: A new video-based hazard perception test was developed, using a total of 20 hazardous situation videos plus 8 quasi-hazardous situation videos. They were selected from 167 recordings of natural hazards in real Spanish driving settings

Bayesian Programming Multi-Target Tracking: an Automotive Application

A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. In particular, target tracking is still challenging in urban trafc situations, because of the large number of rapidly maneuvering targets. The goal of this paper is to present an original way to perform target position and velocity, based on the occupancy grid framework. The main interest of this method is to avoid the decision problem of classical multi-target tracking algorithms. Obtained occupancy grids are combined with danger estimation to perform an elementary task of obstacle avoidance with an electric car

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context

Risk Assessment Algorithms Based On Recursive Neural Networks

The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning the structure of the risk. Furthermore, the combination of a rich information encoding procedure with a generalized model of dynamical recurrent networks permit us, as we shall demonstrate, a sophisticated processing of information that we believe as being a first step for building future advanced intersection safety system

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

Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems. However, if the vehicle fails to locate any critical sign, it might make a catastrophic failure. In this paper, we propose an approach to identify traffic signs that have been mistakenly discarded by the object detector. The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign. This approach can be useful to evaluate the performance of the detector during the deployment phase. We trained a single shot multi-box object detector to detect traffic signs and used its internal features to train a separate false negative detector (FND). During deployment, FND decides whether the traffic sign detector (TSD) has missed a sign or not. We are using precision and recall to measure the accuracy of FND in two different datasets. For 80% recall, FND has achieved 89.9% precision in Belgium Traffic Sign Detection dataset and 90.8% precision in German Traffic Sign Recognition Benchmark dataset respectively. To the best of our knowledge, our method is the first to tackle this critical aspect of false negative detection in robotic vision. Such a fail-safe mechanism for object detection can improve the engagement of robotic vision systems in our daily life.Comment: Submitted to the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019