TY - GEN
T1 - Rule-Compliant Trajectory Repairing using Satisfiability Modulo Theories
AU - Lin, Yuanfei
AU - Althoff, Matthias
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Autonomous vehicles must comply with traffic rules. However, most motion planners do not explicitly consider all relevant traffic rules. Once traffic rule violations of an initially-planned trajectory are detected, there is often not enough time to replan the entire trajectory. To solve this problem, we propose to repair the initial trajectory by investigating the satisfiability modulo theories paradigm. This framework makes it efficient to reason whether and how the trajectory can be repaired and, at the same time, determine the part along the trajectory that can remain unchanged. Moreover, the robustness of traffic rule satisfaction is used to formulate a convex optimization problem for generating rule-compliant trajectories. We compare our approach with trajectory replanning and demonstrate its usefulness with traffic scenarios from the CommonRoad benchmark suite and recorded data. The evaluation result shows that rule-compliant trajectory repairing is computationally efficient and widely applicable.
AB - Autonomous vehicles must comply with traffic rules. However, most motion planners do not explicitly consider all relevant traffic rules. Once traffic rule violations of an initially-planned trajectory are detected, there is often not enough time to replan the entire trajectory. To solve this problem, we propose to repair the initial trajectory by investigating the satisfiability modulo theories paradigm. This framework makes it efficient to reason whether and how the trajectory can be repaired and, at the same time, determine the part along the trajectory that can remain unchanged. Moreover, the robustness of traffic rule satisfaction is used to formulate a convex optimization problem for generating rule-compliant trajectories. We compare our approach with trajectory replanning and demonstrate its usefulness with traffic scenarios from the CommonRoad benchmark suite and recorded data. The evaluation result shows that rule-compliant trajectory repairing is computationally efficient and widely applicable.
UR - http://www.scopus.com/inward/record.url?scp=85135381104&partnerID=8YFLogxK
U2 - 10.1109/IV51971.2022.9827357
DO - 10.1109/IV51971.2022.9827357
M3 - Conference contribution
AN - SCOPUS:85135381104
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 449
EP - 456
BT - 2022 IEEE Intelligent Vehicles Symposium, IV 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Intelligent Vehicles Symposium, IV 2022
Y2 - 5 June 2022 through 9 June 2022
ER -