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

Adaptable Emergency Braking Based on a Fuzzy Controller and a Predictive Model

This work presents the implementation of an adaptable emergency braking system for low speed collision avoidance, based on a frontal laser scanner for static obstacle detection, using a D-GPS system for positioning. A fuzzy logic decision process performs a criticality assessment that triggers the emergency braking system and modulates its behavior. This criticality is evaluated through the use of a predictive model based on a kinematic estimation, which modulates the decision to brake. Additionally a critical study is conducted in order to provide a benchmark for comparison, and evaluate the limits of the predictive model. The braking decision is based on a parameterizable braking model tuned for the target vehicle, that takes into account factors such as reaction time, distance to obstacles, vehicle velocity and maximum deceleration. Once activated, braking force is adapted to reduce vehicle occupants discomfort while ensuring safety throughout the process. The system was implemented on a real vehicle and proper operation is validated through extensive testing carried out at Tecnalia facilities.This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 692480. This Joint Undertaking receives support from the European Unions Horizon 2020 research and innovation programme and Germany, Netherlands, Spain, Austria, Belgium, Slovakia

A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

RESPONSE TIME TO HAZARD: THE ROLE OF ATTENTION, DECISION MAKING AND EMOTIONS ON EXPECTATIONS IN REAL-LIFE AND VIRTUAL DRIVING

Lo scopo della presente ricerca è studiare il peso del fattore umano nei tempi di reazione alla guida. I tempi di reazione son stati studiati sin dalle origini della Psicologia sperimentale, tuttavia se applicati alla guida risulta obsoleto a causa delle specifiche condizioni in cui la reazione si svolge, ai cambiamenti del traffico moderno e ai nuovi dispositivi di supporto intelligente. In letteratura emerge chiaramente l’influenza sul tempo di reazione delle aspettative, della salienza della risposta, della percezione del rischio, dei carichi cognitivi e delle condizioni di rilevazione. La presente ricerca si prefigge di affrontare l’impatto e le modalità di influenza di questi aspetti psicologici sui tempi di reazione alla guida. In particolare i dati registrati in condizioni di guida ecologica reale saranno usati per a) studiare l’influenza delle aspettative sui processi attentivi, emozionali e di presa di decisione alla guida in risposta al pericolo, e b) per valutare l’influenza di diversi livelli di realismo di simulazioni e simulatori virtuali sui processi psicologici che determinano l’IPTR. I risultati mostrano differenze significative nelle diverse fasi che compongono l’IPTR nelle diverse condizioni. I simulatori di guida si sono rivelati avere una validità relativa, ma non assoluta rispetto ai processi attivati nelle condizioni ecologiche, dimostrandosi però in grado di ricreare e modificare coerentemente i processi di avvistamento del pericolo in funzione della prevedibilità dello stesso; rendendoli strumenti utili per l’apprendimento. La ricerca fornisce informazioni sul funzionamento dei processi cognitivi ed emotivi alla guida utili per la ricostruzione degli incidenti, la sicurezza e la prevenzione stradale.The aim of the present research is to study the role of human factor in a salient driving ability for road accident prevention, that is reaction time to danger. Reaction times (RTs) have been investigated since the origin of experimental Psychology, however when applied to driving, the values became obsolete due to modern driving conditions and interaction with advance driving automatic systems and devices. The influence of expectation, urgency, risk perception, cognitive load and driving conditions on the process that determine RTs have been steadily proven in literature. The present research aims to tackle the influence of these factors on RTs while driving. In particular data measured in real-life driving are used to a) study the influence of expectation on attention, emotions and decision making process, and b) assess the influence of virtual settings with different levels of realism, on the psychological process that determine RTs. A specific task that manipulate driver’s expectations was created to assess the influence of attention and decision making process in the different context on RTs. Results show significant differences in the RTs phases, for different situation. Driving simulators with different levels of realism proved to not have absolute validity, but rather relative on the meanings and learning process in detecting danger and deciding what response foster; giving us interesting information for drivers education, road safety and accident reconstruction