TY - GEN
T1 - Set-membership estimation in shared situational awareness for automated vehicles in occluded scenarios
AU - Narri, Vandana
AU - Alanwar, Amr
AU - Martensson, Jonas
AU - Noren, Christoffer
AU - Dal Col, Laura
AU - Johansson, Karl Henrik
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/11
Y1 - 2021/7/11
N2 - One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to adjust the ego-vehicle speed appropriately to maintain a proper safety margin. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety.
AB - One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to adjust the ego-vehicle speed appropriately to maintain a proper safety margin. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety.
UR - http://www.scopus.com/inward/record.url?scp=85118857328&partnerID=8YFLogxK
U2 - 10.1109/IV48863.2021.9575828
DO - 10.1109/IV48863.2021.9575828
M3 - Conference contribution
AN - SCOPUS:85118857328
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 385
EP - 392
BT - 32nd IEEE Intelligent Vehicles Symposium, IV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE Intelligent Vehicles Symposium, IV 2021
Y2 - 11 July 2021 through 17 July 2021
ER -