Obtain a Simulation Model of a Pedestrian Collision Imminent Braking System Based on the Vehicle Testing Data

Forward pedestrian collision imminent braking (CIB) systems has proven to be of great significance in improving road safety and protecting pedestrians. Since pedestrian CIB technology is not mature, the performance of different pedestrian CIB systems varies significantly. Therefore the simulation of a CIB system needs to be vehicle specific. The CIB simulation can be based on the component sensor parameters and decision making rules. Since these parameters and decision rules for on the market vehicles are not available outside of vehicle manufactures, it is difficult for the general research communities to develop a good CIB simulation model based on this approach. To solve this problem, this study presents a new method for developing a pedestrian CIB simulation model using pedestrian CIB testing data. The implementation was in PreScan. The simulation results demonstrate that a pedestrian CIB simulation model developed using this methodology could reflect the behavior of a real vehicle equipped with pedestrian CIB system

Pedestrian Protection Using the Integration of V2V and the Pedestrian Automatic Emergency Braking System

Vehicle to Vehicle (V2V) communication systems enable vehicles to communicate with each other and use the shared information to make safety related decisions. However, the safety improvement of the current V2V systems only benefits V2V-enabled objects in the V2V network. The Pedestrian Automatic Emergency Braking System (PAEB) can utilize onboard sensors to detect pedestrians and make safety related actions so it benefits the individual vehicle and the pedestrians detected by its PAEB. To further improve pedestrian safety, the idea for integrating the capabilities of V2V and PAEB (V2V-PAEB) has been proposed, which allows the information of pedestrians detected by onboard sensors of a vehicle to be shared in the V2V network. A V2V-PAEB enabled vehicle uses not only its onboard sensors, but also received V2V messages from others to detect potential collisions with pedestrians and make better safety related decisions. In this paper, a Matlab/Simulink based simulation model of V2V-PAEB system is presented for demonstrating the proper architecture and information processing processes, and for providing the quick start of developing a better simulation model of V2V-PAEB. The proposed model has also been tested in PreScan simulation environment

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

Car crashes with two-wheelers in China: Proposal and assessment of C-NCAP automated emergency braking test scenarios

In China, around 15,000 users of two-wheelers (TWs) die on the road every year. Passenger cars are the dominating crash opponent of TWs in road traffic crashes. Understanding the characteristics of car crashes with TWs is essential to enhance cars’ safety performance and improve the safety of TW riders in China. This thesis has three objectives. First, to define test scenarios of Automated Emergency Braking systems for cars encountering TWs (TW-AEB) in China (Paper I). Second, to assess whether cars with good ratings in consumer safety rating programs (e.g., New Car Assessment Program: NCAP) are also likely to perform well in the real-world. Finally, to understand the characteristics of the car crashes with TWs after the TW-AEB application. To achieve the first objective, cluster analysis was applied to the China In-Depth Accident Study (CIDAS). The results were six test scenarios (Paper I), which are proposed for the Chinese NCAP (C-NCAP) TW-AEB testing. To achieve the second and third objectives, counterfactual virtual simulations were performed with and without TW-AEB to a) a C-NCAP TW-AEB test scenario set ; b) an alternative scenario set based on the results of Paper I; and c) real-world crashes in China. Results show much higher crash avoidance rate and lower impact speed were found for C-NCAP scenario set than for the other two sets. To better reflect car crashes with TW in China, longitudinal same-direction scenarios with the car or TW turning and perpendicular scenarios with high TW traveling speed are recommended to be included in C-NCAP future releases. Future work will focus on assessing the combined benefit of preventive and protective safety systems for car-to-TW crashes in China

Adversarial attack on a deep model of pedestrian detection in CARLA simulator

L'assistència a la conducció i la conducció autònoma utilitzen models profunds de percepció per dur a terme tasques com la detecció i classificació d'objectes. Un cop entrenats els models, és important trobar casos en què aquests puguin fallar amb l'objectiu d'incloure'ls en un posterior reentrenament i aconseguir models més robusts. Una manera de trobar situacions no contemplades és a través de la simulació, gràcies a la qual es poden forçar casos extrems. En aquest context, aquest treball s'enfoca en la detecció de vianants per imatge utilitzant el simulador CARLA a partir de la creació automàtica d'escenaris per tal de validar les prestacions del detector. Mitjançant un algorisme genètic s'automatitza la cerca d'escenaris plausibles destinades a fer fallar el detector, mostrant-ne així les debilitats. Aquest procediment de validació del detector es considera un mètode d'atac adversari.La asistencia a la conducción y la conducción autónoma utilizan modelos profundos de percepción para realizar tareas como la detección y clasificación de objetos. Una vez entrenados los modelos, es importante encontrar casos en los que éstos puedan fallar con el objetivo de incluirlos en un posterior reentrenamiento y conseguir modelos más robustos. Una forma de encontrar situaciones no contempladas es a través de la simulación, gracias a la cual se pueden forzar casos extremos. En este contexto, este trabajo se enfoca en la detección de peatones por imagen utilizando el simulador CARLA a partir de la creación automática de escenarios para validar las prestaciones del detector. Mediante un algoritmo genético se automatiza la búsqueda de escenarios plausibles destinadas a hacer fallar el detector, mostrando así sus debilidades. Este procedimiento de validación del detector se considerará como un método de ataque adversario.Driving assistance and autonomous driving use deep perception models to perform tasks such as object detection and classification. Once the models have been trained, it is important to find cases where they can fail, with the aim of including them in subsequent retraining and achieving more robust models. One way to find unforeseen situations is through simulation, thanks to which corner cases can be forced. In this context, this project focuses on pedestrian detection by image using CARLA simulator, based on automatic creation of scenarios in order to validate the performance of the detector. One genetic algorithm automates the search for plausible scenarios designed to cause the detector to fail, thus exposing its weaknesses. This detector validation procedure is considered an adversarial attack method

Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT

In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV). The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets. This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols. The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety

Situational Awareness Enhancement for Connected and Automated Vehicle Systems

Recent developments in the area of Connected and Automated Vehicles (CAVs) have boosted the interest in Intelligent Transportation Systems (ITSs). While ITS is intended to resolve and mitigate serious traffic issues such as passenger and pedestrian fatalities, accidents, and traffic congestion; these goals are only achievable by vehicles that are fully aware of their situation and surroundings in real-time. Therefore, connected and automated vehicle systems heavily rely on communication technologies to create a real-time map of their surrounding environment and extend their range of situational awareness. In this dissertation, we propose novel approaches to enhance situational awareness, its applications, and effective sharing of information among vehicles.;The communication technology for CAVs is known as vehicle-to-everything (V2x) communication, in which vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) have been targeted for the first round of deployment based on dedicated short-range communication (DSRC) devices for vehicles and road-side transportation infrastructures. Wireless communication among these entities creates self-organizing networks, known as Vehicular Ad-hoc Networks (VANETs). Due to the mobile, rapidly changing, and intrinsically error-prone nature of VANETs, traditional network architectures are generally unsatisfactory to address VANETs fundamental performance requirements. Therefore, we first investigate imperfections of the vehicular communication channel and propose a new modeling scheme for large-scale and small-scale components of the communication channel in dense vehicular networks. Subsequently, we introduce an innovative method for a joint modeling of the situational awareness and networking components of CAVs in a single framework. Based on these two models, we propose a novel network-aware broadcast protocol for fast broadcasting of information over multiple hops to extend the range of situational awareness. Afterward, motivated by the most common and injury-prone pedestrian crash scenarios, we extend our work by proposing an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection for vulnerable road users. Finally, as humans are the most spontaneous and influential entity for transportation systems, we design a learning-based driver behavior model and integrate it into our situational awareness component. Consequently, higher accuracy of situational awareness and overall system performance are achieved by exchange of more useful information