Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios

A Review on Current eCall Systems for Autonomous Car Accident Detection

The aim of the paper is to give an overview on the existing eCall solutions for autonomous car accident detection. The requirements and expectations for such systems, considering both technological possibilities, legal regulatory criteria and market demands are discussed. Sensors utilized in e-call systems (crash sensing, systems for positional and velocity data, and communication solutions) are overviewed in the paper. Furthermore, the existing solutions for eCall devices are compared based on their level of autonomy, technical implementation and provided services

Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT

In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%)

Improving the detection of the transport mode in the MobilitApp Android application

Millorar els algorismes de detecció del medi de transport dels ciutadans de l'Àrea Metropolitana de Barcelona.El objetivo ha sido el de mejorar la aplicación MobilitApp ya existente, para ello se han añadido dos módulos. El primer módulo que se ha añadido ha sido un detector de accidentes. Debido a que en los anteriores trabajos se estaba tratando con el acelerómetro, nos pareció interesante intentar sacar más provecho al acelerómetro e incluir la funcionalidad de poder detectar cuando el móvil ha sufrido un impacto. Una vez el impacto ha sido detectado se envía un mensaje de emergencia. El segundo módulo consiste en un podómetro, MobilitApp cuenta los pasos del usuario y se presenta junto a una aproximación de la distancia recorrida y del número de calorías quemadas. Como último paso, hemos realizado un análisis sobre las gráficas que se han obtenido de los usuarios tomadas del acelerómetro sobre metro o tren, y hemos comprobado su parecido con una señal sinusoidal. Esta característica ayudará en futuros trabajos para identificar mejor el tipo de sistema de transporte utilizado por el usuario. Además, sería de gran ayuda para ahorrar batería ya que la señal GPS sería menos utilizada.This study aims to improve an existing application for citizen mobility analytics: MobilitApp. Two modules were added to the app in order to develop the desired functionalities. The first module has been added is an accident detector. Because of the previous work involves dealing with the accelerometer, it seemed interesting to try to get more out of the accelerometer and include the functionality to detect when the phone has suffered an impact. Once the impact has been detected, an emergency message is sent. The second module consists of a pedometer, MobilitApp counts the number of the user’s steps and presents them with an approximation of the distance walked and the number of calories burned. As a last step, we have made an analysis of the graphs that have been obtained from the user’s accelerometers travelling on train or subway, and we have checked their resemblance to a sinusoidal signal. This feature will help in future works to better identify the type of public transportation system being used by the user. Besides, it would help to save battery since the GPS signal will be less used.L’objectiu ha estat el de millorar l’aplicació MobilitApp ja existent, per això s’han afegit dos mòduls. El primer mòdul que s’ha afegit ha estat un detector d’accidents. A causa que en els anteriors treballs s’estava tractant amb l’acceleròmetre, ens va semblar interessant intentar treure més profit a l’acceleròmetre e incloure la funcionalitat de poder detectar quan el mòbil ha patit un impacte. Un cop l’impacte ha estat detectat s’envia un missatge d’emergència. El segon mòdul consisteix en un podòmetre, MobilitApp compte els passos de l’usuari i se’ls presenta amb una aproximació de la distància recorreguda y de les calories cremades. Com a últim pas, em fet un anàlisi sobre les gràfiques que s’han obtingut dels usuaris preses de l’acceleròmetre sobre metro o tren, i hem comprovat la seva semblança amb una senyal sinusoïdal. Aquesta característica ens ajudarà en treballs futurs per identificar millor el tipus de sistema de transport utilitzat per l’usuari. A més, seria de gran ajuda per estalviar bateria ja que el senyal GPS seria menys utilitzat

App-based feedback on safety to novice drivers: learning and monetary incentives

An over-proportionally large number of car crashes is caused by novice drivers. In a field experiment, we investigated whether and how car drivers who had recently obtained their driving license reacted to app-based feedback on their safety-relevant driving behavior (speeding, phone usage, cornering, acceleration and braking). Participants went through a pre-measurement phase during which they did not receive app-based feedback but driving behavior was recorded, a treatment phase during which they received app-based feedback, and a post-measurement phase during which they did not receive app-based feedback but driving behavior was recorded. Before the start of the treatment phase, we randomly assigned participants to two possible treatment groups. In addition to receiving app-based feedback, the participants of one group received monetary incentives to improve their safety-relevant driving behavior, while the participants of the other group did not. At the beginning and at the end of experiment, each participant had to fill out a questionnaire to elicit socio-economic and attitudinal information. We conducted regression analyses to identify socio-economic, attitudinal, and driving-behavior-related variables that explain safety-relevant driving behavior during the pre-measurement phase and the self-chosen intensity of app usage during the treatment phase. For the main objective of our study, we applied regression analyses to identify those variables that explain the potential effect of providing app-based feedback during the treatment phase on safety-relevant driving behavior. Last, we applied statistical tests of differences to identify self-selection and attrition biases in our field experiment. For a sample of 130 novice Austrian drivers, we found moderate improvements in safety-relevant driving skills due to app-based feedback. The improvements were more pronounced under the treatment with monetary incentives, and for participants choosing higher feedback intensities. Moreover, drivers who drove relatively safer before receiving app-based feedback used the app more intensely and, ceteris paribus, higher app use intensity led to improvements in safety-related driving skills. Last, we provide empirical evidence for both self-selection and attrition biases