Kinematic trajectory prediction algorithms: BYDRecent Research Landscape
Uncertainty in surrounding vehicle movement creates collision risks that necessitate high computational overhead. These innovations utilize predictive modeling to stabilize path planning and reduce real-time processing latency.
What technical problems is BYD addressing in Kinematic trajectory prediction algorithms?
Inaccurate dynamic path projection
(60)evidences
Inconsistent coordination between planned paths and real-time vehicle dynamics leads to unsafe maneuvers. Resolving this ensures deterministic behavior in complex driving environments.
Inaccurate low speed maneuvering
(47)evidences
Manual parking errors and sensor-to-actuation latency cause collisions or misalignment in tight spaces. Precise trajectory control eliminates human error and physical damage during low-speed navigation.
Unreliable sensor data fusion
(32)evidences
Uncertainty in surface traction and vehicle velocity leads to catastrophic failure in kinematic path planning. Accurate friction identification prevents loss of control during maneuvers.
Unreliable lateral maneuver intent
(22)evidences
Ambiguity in current lane positioning and future maneuver selection leads to unsafe trajectory planning. Resolving this uncertainty prevents collisions during high-speed lane transitions and improves autonomous navigation reliability.
Inaccurate spatial localization
(22)evidences
Inconsistent coordinate frame alignment and mapping errors prevent reliable vehicle positioning. Resolving this ensures safe navigation and precise trajectory execution in dynamic environments.