You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.
The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.
For the reports in this repository we specifically note that
- the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
- the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
- technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
- in other cases, please contact the copyright owner for detailed information
By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.
If you are in doubt, feel free to contact webmaster@ide.mdh.se
Analyzing Inter-Vehicle Collision Predictions during Emergency Braking with Automated Vehicles
Publication Type:
Conference/Workshop Paper
Venue:
2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications
Abstract
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.
Bibtex
@inproceedings{Gorospe6701,
author = {Joseba Gorospe and Shahriar Hasan and Mir Riyanul Islam and Arrate Alonso G{\'o}mez and Svetlana Girs and Elisabeth Uhlemann},
title = {Analyzing Inter-Vehicle Collision Predictions during Emergency Braking with Automated Vehicles},
pages = {1--8},
month = {August},
year = {2023},
booktitle = {2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications},
publisher = {IEEE},
url = {http://www.es.mdu.se/publications/6701-}
}