Autonomous Vehicle Control Under Uncertainty With Chance-Constrained Sensing
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating uncertain environments due to incomplete knowledge of their surroundings, leading to potential collisions and inefficient motion planning, as existing solutions fail to effectively incorporate the impact of sensing actions on future uncertainty and non-convex collision avoidance constraints.
Innovation Solution
A multi-stage Perception-aware Chance-constrained Model Predictive Control (PAC-MPC) method that predicts future information acquisition and accounts for the behavior of actors in the environment, using stochastic vectors and probabilistic constraints to ensure safe and efficient motion planning by convexifying non-convex constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle operates cautiously to avoid unsafe events in uncertain environment, then safety is improved, but the motion becomes overly conservative resulting in degraded performance such as longer time to reach goal and more energy used
Solution Approach 1:
The control strategy dynamically adjusts the balance between safety and performance by incorporating real-time uncertainty information from sensors into the optimization framework. The vehicle transitions from overly conservative behavior to adaptive behavior that accepts calculated risks when uncertainty is low, improving performance while maintaining safety guarantees through probabilistic constraints.
Solution Approach 2:
The invention changes the parameter representation of safety from deterministic to probabilistic constraints. By formulating safety as chance constraints with user-defined confidence levels, the system allows performance optimization within statistically guaranteed safety margins, resolving the contradiction between cautious safety and efficient performance.
2Measurement precision
If the autonomous vehicle acquires more information on the environment through sensing, then knowledge of object state becomes more precise, but the motion may deviate significantly from the goal resulting in degradation of performance
Solution Approach 1:
The system applies partial sensing action by selectively acquiring information only where and when it is needed for safe and efficient navigation. Rather than maximizing information acquisition, the optimization framework determines the minimal sufficient sensing required to maintain safety guarantees while achieving performance goals, preventing performance degradation from excessive information-seeking behavior.
Solution Approach 2:
The invention performs preliminary action by predicting future uncertainty and information acquisition before executing motion commands. The multi-stage optimization anticipates how current sensing and motion decisions will affect future knowledge state, allowing the vehicle to plan information-gathering maneuvers that advance both understanding and goal achievement simultaneously.
3Loss of information
If the autonomous vehicle stays within the range and line-of-sight of sensors to acquire more information, then the amount of information acquired increases, but going too close to assumed position of objects before enough knowledge is available may present risks
Solution Approach 1:
The system implements feedback by continuously updating the uncertainty model based on actual sensor measurements and using this updated information to adjust subsequent motion and sensing decisions. The closed-loop optimization ensures that the vehicle responds to actual environmental feedback rather than relying solely on assumed positions, reducing risks while maximizing information acquisition.
Solution Approach 2:
The invention applies beforehand cushioning by incorporating safety margins and probabilistic constraints before the vehicle approaches uncertain objects. The chance constraints pre-establish safe operating boundaries that account for measurement uncertainty, allowing the vehicle to approach objects more aggressively than traditional methods while maintaining predetermined safety buffers.
4Reliability
If the autonomous vehicle plans motion based on available knowledge, then the need to avoid unsafe events is addressed, but the motion becomes overly conservative and stays on the most known path resulting in degraded performance
Solution Approach 1:
The motion planning dynamically adapts to uncertainty levels rather than following fixed conservative paths. The multi-stage optimization recalculates optimal trajectories based on updated environmental knowledge, allowing the vehicle to efficiently exploit well-known areas while carefully navigating uncertain regions, significantly reducing time loss compared to static conservative planning.
Solution Approach 2:
The invention changes the safety constraint parameter from deterministic to probabilistic formulation. By expressing safety as chance constraints with confidence levels rather than absolute guarantees, the system enables more aggressive and time-efficient motion plans that accept statistically small risk levels, reducing the time penalty associated with overly conservative deterministic safety margins.
Data Source
AI summary
The present disclosure provides a controller for controlling an ego vehicle in an uncertain environment. The controller is caused to acquire knowledge of the environment from measurements associated with sensors the ego. The measurements are based on a state of the ego vehicle and sensing instructions associated with controlling an operation of the sensors. The controller is further caused to estimate a state of the environment, including uncertainty of a state of the at least one moving object or obstacle in the environment. Further a sequence of control inputs is determined by solving a multivariable and a multistage stochastic constrained optimization of a model of the motion of the ego vehicle. The controller is then caused to control the ego vehicle and the sensors based on the sequence of control inputs and the sequence of sensing instructions.