Autonomous Vehicle Obstacle Avoidance State Machine
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating through environments with dynamic and static obstacles, as existing methods often result in unnecessary stops and delays, and may not efficiently determine safe trajectories to reach intended destinations.
Innovation Solution
The system determines a target trajectory by analyzing map and sensor data to identify drivable regions and obstacles, calculating costs associated with different actions, and selecting the most efficient path, such as using an oncoming lane to circumvent obstacles, thereby optimizing navigation and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses traditional obstacle avoidance methods, then safety is maintained, but unnecessary stops and delays occur
Solution Approach 1:
The system dynamically transitions between multiple operating states (first state for nominal trajectory following, second state for obstacle avoidance, third state for returning to trajectory) based on real-time obstacle detection and cost evaluation. This dynamic state management allows the vehicle to adapt its behavior to current conditions, maintaining safety while minimizing unnecessary stops and delays.
Solution Approach 2:
The system changes key operational parameters including trajectory selection (nominal vs. alternate), speed adjustments during different states, and transition timing based on cost calculations. By dynamically adjusting these parameters based on real-time conditions, the system achieves safer navigation while reducing time loss compared to traditional static obstacle avoidance methods.
2Productivity
If the autonomous vehicle follows a strict nominal trajectory, then path efficiency is maintained, but collision risk with obstacles increases
Solution Approach 1:
The system continuously monitors the environment for obstacles and evaluates cost functions that balance trajectory adherence with collision avoidance. This feedback mechanism allows the vehicle to determine when to transition from the nominal trajectory to an alternate trajectory, maintaining path efficiency while reducing collision risk through real-time decision making.
Solution Approach 2:
The trajectory selection is dynamic rather than static. The system can switch between following the nominal trajectory and using an alternate trajectory based on real-time obstacle detection and cost evaluation, thereby maintaining overall path efficiency while adapting to avoid collisions.
3Reliability
If the autonomous vehicle frequently changes trajectory to avoid obstacles, then collision avoidance improves, but navigation complexity increases
Solution Approach 1:
The navigation system is segmented into distinct operational states (first state for nominal following, second state for obstacle avoidance, third state for return to trajectory). Each state has clearly defined transition conditions based on cost function evaluations, which simplifies the overall navigation complexity while maintaining effective collision avoidance through structured decision making.
Data Source
AI summary
A vehicle can traverse an environment along a first region and detect an obstacle impeding progress of the vehicle. The vehicle can determine a second region that is adjacent to the first region and associated with a direction of travel opposite the first region. The vehicle can use a state machine to determine an action (e.g., an oncoming action) to utilize the second region to overtake the obstacle. By comparing a cost to a cost threshold and/or to a cost associated with another action (e.g., a “stay in lane” action), the vehicle, using the state machine, can determine a target trajectory that traverses through the second region and can traverse the environment based on the target trajectory to avoid, for example, the obstacle in the environment while maintaining a safe distance from the obstacle and/or other entities in the environment.