Título
Analyzing Inter-Vehicle Collision Predictions during Emergency Braking with Automated VehiclesOtras instituciones
Mälardalen UniversityVersión
Postprint
Derechos
© 2023 IEEEAcceso
Acceso embargadoVersión del editor
https://doi.org/10.1109/WiMob58348.2023.10187826Publicado en
International Conference on Wireless and Mobile Computing, Networking and Communications 2023. Vol. June. Pp. 411-418Editor
IEEEPalabras clave
Wireless communication
analytical modeling
Wireless sensor networks
Automation ... [+]
analytical modeling
Wireless sensor networks
Automation ... [+]
Wireless communication
analytical modeling
Wireless sensor networks
Automation
Machine learning
Predictive model
Sensor systems
ODS 9 Industria, innovación e infraestructura [-]
analytical modeling
Wireless sensor networks
Automation
Machine learning
Predictive model
Sensor systems
ODS 9 Industria, innovación e infraestructura [-]
Resumen
Automated Vehicles (AVs) require sensing and perception to integrate data from multiple sources, such as cameras, lidars, and radars, to operate safely and efficiently. Collaborative sensing through w ... [+]
Automated Vehicles (AVs) require sensing and perception to integrate data from multiple sources, such as cameras, lidars, and radars, to operate safely and efficiently. Collaborative sensing through wireless vehicular communications can enhance this process. However, failures in sensors and communication systems may require the vehicle to perform a safe stop or emergency braking when encountering hazards. By identifying the conditions for being able to perform emergency braking without collisions, better automation models that also consider communications need to be developed. Hence, we propose to employ Machine Learning (ML) to predict inter-vehicle collisions during emergency braking by utilizing a comprehensive dataset that has been prepared through rigorous simulations. Using simulations and data-driven modeling has several advantages over physics-based models in this case, as it, e.g., enables us to provide a dataset with varying vehicle kinematic parameters, traffic density, network load, vehicle automation controller parameters, and more. To further establish the conditions for inter-vehicle collisions, we analyze the predictions made through interpretable ML models and rank the features that contribute to collisions. We also extract human-interpretable rules that can establish the conditions leading to collisions between AVs during emergency braking. Finally, we plot the decision boundaries between different input features to separate the collision and non-collision classes and demonstrate the safe region of emergency braking. [-]
Financiador
Eusko Jaurlaritza = Gobierno VascoEusko Jaurlaritza = Gobierno Vasco
European Commission
European Commission
Programa
Ikertalde Convocatoria 2022-2023Elkartek 2021
H2020
H2020-ECSEL
Número
IT1451-22KK-2021-00123
764951
101007350
URI de la ayuda
Sin informaciónSin información
https://doi.org/10.3030/764951
https://doi.org/10.3030/101007350
Proyecto
Teoría de la Señal y ComunicacionesEvolución tecnológica para la automatización multivehicular y evaluación de funciones de conducción altamente automatizadas (AUTOEV@L)
Immersive Visual Technologies for Safety-critical Applications (ImmerSAFE)
AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems (AIDOaRt)
Colecciones
- Congresos - Ingeniería [341]