Vehicle Trajectory Reset Control Using Ambient Environment Feedback
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Solution Overview
Problem
Existing vehicle control systems fail to dynamically adjust the optimal traveling trajectory for autonomous vehicles in response to changes in the ambient environment, leading to suboptimal performance over time.
Innovation Solution
A vehicle control system comprising a server, infrastructure sensor, and onboard device that continuously monitors and adjusts the traveling trajectory using real-time ambient environment data and vehicle characteristics to determine and reset the optimal path as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the traveling trajectory is determined once based on initial ambient environment data, then the calculation load is reduced and processing speed is improved, but the trajectory becomes suboptimal when the environment changes
Solution Approach 1:
The system performs periodic re-determination of the traveling trajectory at predetermined intervals or when trigger conditions are met. The determination unit re-executes trajectory optimization using updated ambient environment data, ensuring the trajectory remains optimal while balancing computational load through scheduled rather than continuous re-calculation.
Solution Approach 2:
The system establishes a feedback loop where the determination unit continuously monitors ambient environment data and compares it with the current traveling trajectory. When environmental changes exceed a threshold or trigger conditions are satisfied, the system feeds back new trajectory calculations to update the vehicle's path, ensuring adaptability while maintaining operational efficiency.
2Adaptability or versatility
If the traveling trajectory is continuously re-determined based on real-time ambient environment data, then the trajectory optimality is improved, but the processing load increases and may cause delays
Solution Approach 1:
The system dynamically adjusts the trajectory re-determination frequency and trigger conditions based on the rate of environmental change and vehicle operational state. When environmental conditions change rapidly, the system increases re-determination frequency; when conditions are stable, it reduces frequency to minimize processing load and system complexity.
Solution Approach 2:
The system changes key parameters such as re-determination time intervals, trigger thresholds, and evaluation criteria based on ambient environment characteristics and vehicle state. This allows the system to adapt its complexity level to match the actual need for trajectory optimization, reducing unnecessary computations while maintaining optimality when needed.
Data Source
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AI summary
A server 100 includes: a candidate trajectory generation part 102 for generating a candidate traveling trajectory of a target vehicle; a trajectory simulator 107 for executing a trajectory simulation on the candidate traveling trajectory generated by the candidate trajectory generation part 102; a trajectory evaluation part 108 for determining a traveling trajectory based on a result of the trajectory simulation; a vehicle coordination part 109 for transmitting information of the traveling trajectory determined by the trajectory evaluation part 108 to an onboard device 300; an infrastructure coordination part 104 for obtaining sensing information from an infrastructure sensor 200; an ambient environment generation part 105 for generating information indicating an ambient environment of the traveling trajectory, based on the sensing information obtained by the infrastructure coordination part 104; and a resetting determination part 110 for determining whether to reset the traveling trajectory.