Autonomous Trajectory Validation for Unsafe Lateral Vehicle Interactions
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Solution Overview
Problem
Autonomous driving systems face challenges in validating trajectories generated by statistical models, which lack transparency and can lead to unsafe scenarios, especially when responding to unsafe driving behaviors by other road users, and existing safety models do not adequately address how to validate these trajectories.
Innovation Solution
A computer-implemented method and apparatus that determine whether a planned trajectory is invalid by analyzing the lateral position and velocity of a model vehicle relative to another vehicle, considering conditions such as lateral range and acceleration, to ensure safe and valid trajectories are maintained, even in the presence of unsafe driving behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If statistical models are used for autonomous driving trajectory generation, then the system can handle complex traffic scenarios and adapt to diverse driving behaviors, but the transparency and safety validation of the generated trajectories become insufficient
Solution Approach 1:
The patent introduces a safety validation module as an intermediary between the statistical model-based trajectory generator and the autonomous driving control system. This module checks whether generated trajectories satisfy safety constraints (such as maintaining safe distances from other vehicles, avoiding collisions, and adhering to traffic rules) before executing maneuvers. The validator acts as a mediator that filters out unsafe trajectories while preserving the adaptability benefits of statistical models.
Solution Approach 2:
The system performs preliminary safety validation checks on generated trajectories before they are executed. The validation process evaluates potential trajectories in advance by simulating their safety implications, checking for conflicts with other road users, and verifying compliance with safety constraints. This preliminary action prevents unsafe maneuvers from being executed, addressing the reliability concern while maintaining the versatility of statistical modeling.
2Reliability
If strict safety validation is applied to all generated trajectories, then unsafe maneuvers are prevented, but valid trajectories representing best responses to dangerous situations may be incorrectly invalidated
Solution Approach 1:
The validation system applies different validation strictness levels to different spatial and contextual regions. In normal driving conditions, strict validation is applied to prevent unsafe maneuvers. However, in dangerous situations where another vehicle is already violating safety constraints (e.g., aggressive lane changes, tailgating), the system relaxes validation for trajectories that represent appropriate defensive responses. This local differentiation allows the system to prevent unsafe actions while permitting necessary reactions to external threats.
Solution Approach 2:
The validation criteria are made dynamic rather than static. The system continuously monitors the behavior of surrounding vehicles and adjusts the stringency of trajectory validation in real-time. When other road users exhibit unsafe behaviors, the validation thresholds are adapted to allow more aggressive defensive maneuvers. This dynamic approach ensures that safety validation remains appropriate to the current traffic context, preventing false invalidation of necessary responses.
3Reliability
If comprehensive trajectory validation is implemented to ensure safety, then the reliability of autonomous driving is improved, but the computational complexity and processing time increase
Solution Approach 1:
The trajectory validation process is segmented into multiple independent check modules, each responsible for verifying specific safety aspects (e.g., collision avoidance, lane boundary compliance, safe distance maintenance, speed limits). This segmentation allows the system to perform comprehensive validation through a series of simpler, specialized checks rather than one complex monolithic validation process, reducing overall computational complexity while maintaining thorough safety verification.
Solution Approach 2:
The system implements a tiered validation approach where critical safety checks (such as immediate collision risk) are performed with high priority and strict criteria, while less critical checks are performed with lower priority or relaxed criteria. This partial action approach focuses computational resources on the most important safety aspects, achieving adequate validation without the full overhead of exhaustive checking of every possible parameter.
Data Source
AI summary
A method of determining whether a planned trajectory of a first vehicle over a road along which the first vehicle and a second vehicle are traveling, is invalid, comprising: obtaining the planned trajectory, comprising a first state of the first vehicle for each of a plurality of time instants; obtaining a second state of the second vehicle for each time instant; determining, for each time instant, a respective lateral range extending from the second vehicle; and determining that the planned trajectory is invalid where, for the first and second states at one or more of the time instants: the first vehicle is within the lateral range and within a lane boundary region of the road; and a direction of a lateral velocity of the first vehicle is towards the second vehicle and a lateral acceleration of the first vehicle away from the second vehicle is smaller than a predetermined threshold.


