Certainty and Critical Speed for Decision Making in Tests of Pedestrian Automatic Emergency Braking Systems

This paper starts with depicting the test series carried out by the Transportation Active Safety Institute, with two cars equipped with pedestrian automatic emergency braking (AEB) systems. Then, an AEB analytical model that allows the prediction of the crash speed, stopping distance, and stopping time with a high degree of accuracy is presented. The model has been validated with the test results and can be used for real-time application due to its simplicity. The concept of the active safety margin is introduced and expressed in terms of deceleration, time, and distance in the model. This margin is a criterion that can be used either in the design phase of pedestrian AEB for real-time decision making or as a characteristic indicator in test procedures. Finally, the decision making is completed with the analysis of the behavior of the pedestrian lateral movement and the calculation of the certainty of finding the pedestrian into the crash zone. This model of certainty completes the analysis of decision making and leads to the introduction of the new concept of “critical speed for decision making.” All major variables influencing the performance of pedestrian AEB have been modeled. A proposal of certainty scale in this kind of tests and a set of recommendations are given to improve the efficiency and accuracy of evaluation of pedestrian AEB systems.

[1]  J. Hillenbrand,et al.  Situation assessment algorithm for a collision prevention assistant , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[2]  A. López,et al.  Fast Computing on Vehicle Dynamics Using Chebyshev Series Expansions , 2015, IEEE/ASME Transactions on Mechatronics.

[3]  Michelle M. Porter,et al.  Pedestrians' Normal Walking Speed and Speed When Crossing a Street , 2007 .

[4]  Vicente Milanés Montero,et al.  Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller , 2011, IEEE Transactions on Intelligent Transportation Systems.

[5]  S. Cafiso,et al.  Pedestrian Crossing Safety Improvements: Before and After Study using Traffic Conflict Techniques , 2010 .

[6]  A. Polychronopoulos,et al.  Dynamic situation and threat assessment for collision warning systems: the EUCLIDE approach , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[7]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  D. Aubert,et al.  A collision mitigation system using laser scanner and stereovision fusion and its assessment , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[9]  Bo Tang,et al.  Obtain a simulation model of a pedestrian collision imminent braking system based on the vehicle testing data , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[10]  Qiang Yi,et al.  Analysis of the Braking Behaviour in Pedestrian Automatic Emergency Braking , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[11]  Ulrich Sander,et al.  Pedestrian fatality risk as a function of car impact speed. , 2009, Accident; analysis and prevention.

[12]  Qiang Yi,et al.  Mannequin development for pedestrian pre-Collision System evaluation , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[13]  D. Fernandez,et al.  3D Candidate Selection Method for Pedestrian Detection on Non-Planar Roads , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[14]  Xin ZHANG,et al.  Modeling Pedestrian Walking Speed at Signalized Crosswalks Considering Crosswalk Length and Signal Timing , 2013 .

[15]  Qiang Yi,et al.  Joint Motion Pattern of Limb Moving Mannequins for Active Safety Vehicle Tests , 2013 .

[16]  Ignacio Parra,et al.  An Experimental Study on Pitch Compensation in Pedestrian-Protection Systems for Collision Avoidance and Mitigation , 2009, IEEE Transactions on Intelligent Transportation Systems.

[17]  Tarek Sayed,et al.  Surrogate Safety Assessment Model and Validation: Final Report , 2008 .

[18]  Ignacio Parra,et al.  Combination of Feature Extraction Methods for SVM Pedestrian Detection , 2007, IEEE Transactions on Intelligent Transportation Systems.

[19]  Serge P. Hoogendoorn,et al.  Simulation of pedestrian flows by optimal control and differential games , 2003 .

[20]  Makoto Nakai,et al.  Research into Evaluation Method for Pedestrian Pre-Collision System , 2013 .

[21]  David Gerónimo Gómez,et al.  Survey of Pedestrian Detection for Advanced Driver Assistance Systems , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Klaus C. J. Dietmayer,et al.  Early detection of the Pedestrian's intention to cross the street , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[23]  Kristian Kroschel,et al.  A Multilevel Collision Mitigation Approach—Its Situation Assessment, Decision Making, and Performance Tradeoffs , 2006, IEEE Transactions on Intelligent Transportation Systems.

[24]  Cristina Moriano Sánchez Método de procesamiento rápido de las ecuaciones de la dinámica vehicular , 2013 .

[25]  Tomasz Stańczyk,et al.  Researches on the reaction of a pedestrian stepping into the road from the right side from behind and an obstacle realized on the track , 2011 .

[26]  Wassim G Najm,et al.  Target Crashes and Safety Benefits Estimation Methodology for Pedestrian Crash Avoidance/Mitigation Systems , 2014 .

[27]  Dariu Gavrila,et al.  Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle , 2007, International Journal of Computer Vision.

[28]  N. Suganuma,et al.  An Obstacle Extraction Method Using Virtual Disparity Image , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[29]  Dariu Gavrila,et al.  Active Pedestrian Safety by Automatic Braking and Evasive Steering , 2011, IEEE Transactions on Intelligent Transportation Systems.

[30]  Christophe F. Wakim,et al.  A Markovian model of pedestrian behavior , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[31]  A. Fascioli,et al.  Pedestrian Protection Systems : Issues , Survey , and Challenges , 2007 .

[32]  Yi Zhang,et al.  Pedestrian Safety Analysis in Mixed Traffic Conditions Using Video Data , 2012, IEEE Transactions on Intelligent Transportation Systems.

[33]  Yaobin Chen,et al.  Preliminary Benefit Analysis for Pedestrian Crash Imminent Braking Systems , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[34]  Qiang Yi,et al.  Method of Selecting Test Scenarios for Pedestrian Forward Looking Pre-Collision System Evaluation , 2014 .

[35]  S. Gard,et al.  Temporal symmetries during gait initiation and termination in nondisabled ambulators and in people with unilateral transtibial limb loss. , 2005, Journal of rehabilitation research and development.

[36]  K.Ch. Fuerstenberg,et al.  Pedestrian protection using laserscanners , 2005 .

[37]  Koji Suzuki,et al.  Development of Pre-Crash Safety System with Pedestrian Collision Avoidance Assist , 2013 .

[38]  Quantitative gait analysis - comparison of rheumatoid arthritic and non-arthritic subjects. , 1994, The Australian journal of physiotherapy.

[39]  Massimo Bertozzi,et al.  Shape-based pedestrian detection , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[40]  Vivek R. Das,et al.  Pedestrian behaviour under varied traffic and spatial conditions , 2014 .