Autonomous Vehicle Motion Planning Using Passenger Reaction Feedback
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Solution Overview
Problem
Existing autonomous driving technologies fail to account for the diverse driving behaviors of human drivers, which are adaptive to real-time situations and passenger reactions, leading to inadequate planning and control.
Innovation Solution
Implement a self-capability aware, human-like, and personalized planning and control system that incorporates real-time data and a self-aware capability model to adapt vehicle operations, considering both intrinsic and extrinsic factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional route planning and motion planning methods are used, then the autonomous vehicle can operate based on basic traffic regulations and safety rules, but the vehicle cannot adapt to diverse passenger preferences and real-time situational adjustments that human drivers naturally make
Solution Approach 1:
The system continuously monitors passenger reactions to vehicle motion and uses this feedback to adaptively adjust planning parameters. Sensors detect passenger states (e.g., through cameras, microphones, or other detection devices) and the planning module modifies route and motion plans based on detected passenger preferences, creating a closed-loop adaptive system that evolves during operation.
Solution Approach 2:
The planning system transitions from static, pre-programmed routes to dynamic, real-time adaptive planning. The system continuously updates motion parameters, route selections, and behavior patterns based on current passenger reactions and environmental conditions, allowing the vehicle to dynamically adjust its operation rather than following fixed patterns.
2Adaptability or versatility
If generic vehicle kinematic models and feedback controllers are used, then the control system can maintain basic stability and follow planned trajectories, but the vehicle cannot exhibit human-like adaptive driving behaviors that respond to passengers and environmental nuances
Solution Approach 1:
The system learns and adapts to passenger preferences autonomously without requiring explicit programming for each scenario. The planning module automatically adjusts its behavior based on detected passenger reactions, effectively serving the passenger's needs through self-learning and adaptation rather than through pre-programmed responses to every possible situation.
Solution Approach 2:
The system dynamically modifies control parameters such as acceleration rates, turning speeds, lane-changing timing, and route selection based on detected passenger preferences and environmental conditions. This allows the vehicle to change its operational characteristics in real-time, exhibiting human-like adaptability through parameter adjustment rather than fundamental architectural changes.
3Ease of operation
If the system continuously monitors and adapts to passenger reactions in real-time, then the vehicle can provide personalized and comfortable driving experiences, but the computational load and processing requirements increase significantly
Solution Approach 1:
The system focuses computational resources on monitoring and responding to the most significant passenger reactions and critical driving decisions rather than continuously analyzing every possible parameter at maximum detail. This selective monitoring approach provides sufficient personalization and comfort while reducing overall computational burden through prioritized processing.
Data Source
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AI summary
The present teaching relates to method, system, medium, and implementation of automatic motion planning for an autonomous driving vehicle. Information is obtained with respect to a current location of the autonomous driving vehicle operating in a current vehicle motion, wherein the information is to be used to estimate the operational capability of the autonomous driving vehicle with respect to the current location. Sensor data are obtained, via one or more sensors in one or more media types. A reaction of a passenger present in the vehicle with respect to a current vehicle motion is estimated based on the sensor data. Motion planning is performed based on the information with respect to the current location and the reaction of the passenger to the current vehicle motion.