TY - GEN
T1 - Probabilistic Interaction-Aware Occupancy Prediction for Vehicles in Arbitrary Road Scenes
AU - Zechel, Peter
AU - Streiter, Ralph
AU - Bogenberger, Klaus
AU - Goehner, Ulrich
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/26
Y1 - 2019/3/26
N2 - This paper drafts a new interaction-aware approach to predict road user occupancy through vehicle-specific probabilities of presence. Instead of presenting another behavior prediction approach which tries to predict the most likely vehicle positions, the aim is to determine the likelihood of all feasible vehicle positions, called probability of presence. Thus the occupied area derived from this probability of presence blocks less space compared to existing approaches in the field of occupancy prediction. For this purpose, based on a physical model the future presence areas of a vehicle are fully calculated in a discrete consideration. Each movement possibility is evaluated taking into account the statistically typical driver behavior and interactions with static and dynamic objects. This gives rise to a qualified prediction of the future behavior of vehicles which blocks less space compared to all feasible future vehicle positions, like demonstrated in the numerical example.
AB - This paper drafts a new interaction-aware approach to predict road user occupancy through vehicle-specific probabilities of presence. Instead of presenting another behavior prediction approach which tries to predict the most likely vehicle positions, the aim is to determine the likelihood of all feasible vehicle positions, called probability of presence. Thus the occupied area derived from this probability of presence blocks less space compared to existing approaches in the field of occupancy prediction. For this purpose, based on a physical model the future presence areas of a vehicle are fully calculated in a discrete consideration. Each movement possibility is evaluated taking into account the statistically typical driver behavior and interactions with static and dynamic objects. This gives rise to a qualified prediction of the future behavior of vehicles which blocks less space compared to all feasible future vehicle positions, like demonstrated in the numerical example.
KW - advanced computing applications
KW - autonomous vehicles
KW - behavior prediction
KW - occupancy prediction
UR - http://www.scopus.com/inward/record.url?scp=85064120993&partnerID=8YFLogxK
U2 - 10.1109/IRC.2019.00081
DO - 10.1109/IRC.2019.00081
M3 - Conference contribution
AN - SCOPUS:85064120993
T3 - Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019
SP - 423
EP - 424
BT - Proceedings - 3rd IEEE International Conference on Robotic Computing, IRC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Robotic Computing, IRC 2019
Y2 - 25 February 2019 through 27 February 2019
ER -