Autonomous Vehicle Speed Control Tuned to Human Driving Patterns
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Solution Overview
Problem
Autonomous vehicles often perform abrupt or unsafe maneuvers when adjusting speed to avoid obstacles due to inadequate control systems that fail to mimic human driving patterns effectively.
Innovation Solution
A system and method using a reinforcement learning framework to tune speed control parameters of autonomous vehicles to resemble natural human driving behavior by rewarding profiles that follow human driving patterns and penalizing those that deviate, allowing the vehicle to learn and adapt its speed control over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control systems adjust speed to avoid obstacles, then obstacle avoidance is achieved, but abrupt and uncomfortable maneuvers occur
Solution Approach 1:
The system changes the parameters of speed adjustment by using reinforcement learning to optimize acceleration and deceleration profiles. Instead of fixed control parameters, the system learns optimal parameter values that balance obstacle avoidance with smooth, comfortable maneuvers resembling human driving behavior.
Solution Approach 2:
The system implements feedback mechanisms where the autonomous vehicle's speed profile is continuously compared with human driver behavior data. The reinforcement learning framework uses this feedback to adjust control parameters, ensuring that obstacle avoidance maneuvers remain comfortable and natural.
2Measurement precision
If control adjustments are made to follow desired trajectory, then trajectory tracking is improved, but abrupt maneuvers occur
Solution Approach 1:
The system dynamically adjusts control parameters for acceleration and deceleration based on reinforcement learning outcomes. This allows precise trajectory tracking while maintaining smooth maneuvers that match human driving patterns, resolving the conflict between tracking precision and maneuver smoothness.
Solution Approach 2:
The control system transitions from static parameter settings to dynamic parameter adjustment. The reinforcement learning framework enables real-time optimization of control parameters based on current driving conditions, allowing the system to adaptively balance trajectory tracking precision with maneuver smoothness.
3Reliability
If speed control parameters are tuned for safety, then collision avoidance is improved, but driving behavior becomes unnatural
Solution Approach 1:
The system uses feedback from human driver behavior data to tune safety parameters. By continuously comparing autonomous vehicle responses with human driver patterns and adjusting parameters accordingly, the system achieves both collision avoidance and natural driving behavior.
Solution Approach 2:
The reinforcement learning framework enables the system to self-optimize its safety parameters by learning from observed human driving patterns. The system serves itself by automatically adjusting parameters to balance safety requirements with naturalistic driving behavior without manual intervention.
Data Source
AI summary
A system and method for using human driving patterns to manage speed control for autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to desired human driving behaviors; training a human driving model module using a reinforcement learning process and the desired human driving behaviors; receiving a proposed vehicle speed control command; determining if the proposed vehicle speed control command conforms to the desired human driving behaviors by use of the human driving model module; and validating or modifying the proposed vehicle speed control command based on the determination.