Trailer Parking Control Using Actuator State Prediction
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Solution Overview
Problem
Existing vehicle parking assistance systems struggle to assist in parking vehicles with trailers, as they do not consider the vehicle's state, leading to difficulties in determining suitable parking locations due to limitations in reflecting the trailer's turning radius and bending angle.
Innovation Solution
A system and method that involve a vehicle acquiring state information from sensors to determine the normal behavior of actuators, requesting a new target parking location from a server if unable to travel to the initial location, and using a Kalman filter or pre-learned results to predict actuator states for determining normal behavior and calculating suitable parking locations based on actuator states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing parking assistance systems use only surrounding environment information without reflecting vehicle state, then the system complexity is reduced, but the parking assistance accuracy deteriorates for vehicles with trailers
Solution Approach 1:
The system implements feedback by continuously monitoring actuator state information (steering angle, braking force, driving torque) and using this feedback to adjust parking location selection. The vehicle state information is fed back to the server to determine whether the vehicle can actually reach the suggested parking location, enabling dynamic adjustment of parking assistance based on real vehicle conditions.
Solution Approach 2:
The system performs preliminary action by predicting actuator state information using Kalman filter or pre-learned results before the vehicle actually reaches the parking location. This prediction allows the system to pre-assess whether the vehicle can reach the target parking location based on current actuator performance, and request alternative parking locations in advance if the predicted state indicates inability to reach the target.
2Reliability
If the system requests a new target parking location when actuators are in abnormal state, then the parking feasibility is improved, but the time consumption increases
Solution Approach 1:
The system performs preliminary assessment by predicting actuator state information using Kalman filter or pre-learned results before attempting to reach the parking location. This allows the system to identify potential feasibility issues in advance and request alternative parking locations proactively, rather than discovering inability to reach after already traveling to the target location, thus reducing time loss.
Solution Approach 2:
The vehicle autonomously monitors its own actuator state information and automatically determines whether it can reach the target parking location based on predicted versus actual actuator performance. The vehicle independently requests new parking locations when feasibility is compromised, without requiring manual intervention from the driver, thereby minimizing time consumption while maintaining high reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables effective parking assistance for vehicles with trailers by considering the vehicle's state, improving parking efficiency and reducing congestion in areas with multiple vehicle entries and exits.
Implementation Method 1
the vehicle may predict the state information of the actuator based on a Kalman filter or a pre-learned result, and compare the predicted state information of the actuator with the state information of the actuator acquired from the sensor to determine whether the behavior is in the normal state
Data Source
AI summary
A system for controlling travel of a vehicle includes a server configured to create a target parking location, and the vehicle configured to acquire state information of an actuator from a sensor, determine whether a behavior of the actuator is in a normal state based on the state information of the actuator, determine whether the vehicle is able to travel to the target parking location when it is determined that the behavior of the actuator is in an abnormal state, and request the server for a new target parking location when it is determined that the vehicle is not able to travel to the target parking location.