Adversarial Agent Scenario Generation for Rare AV Behavior
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Solution Overview
Problem
Autonomous vehicles face challenges in anticipating and handling infrequent anomalous human behavior, such as sudden swerving or illegal turns, due to the unpredictability of human actions, which can lead to difficulties in preparing for all contingencies and identifying scenarios that may result in vehicle violations of operating constraints.
Innovation Solution
The development of an adversarial agent component and scenario modification techniques, where a machine-learned model generates a simulated agent to decrease the vehicle's performance score by simulating adverse scenarios, and modifying scenario data to increase the vehicle's interaction capabilities, thereby improving its handling of various situations safely and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional simulation methods are used to test autonomous systems, then common scenarios can be covered, but infrequent anomalous behavior and difficult scenarios cannot be anticipated
Solution Approach 1:
Instead of having the autonomous vehicle try to predict all possible human behaviors, the patent inverts the approach by creating an adversarial agent that actively tries to fool or challenge the autonomous system. This adversarial agent generates anomalous scenarios that test the boundaries of the autonomous vehicle's prediction capabilities, thereby improving its ability to handle rare and difficult scenarios without requiring exhaustive prediction of all possible human actions
Solution Approach 2:
The patent applies preliminary action by pre-training the adversarial agent using historical sensor data and logged scenarios before deployment. This pre-training phase allows the adversarial agent to learn from real-world data patterns and prepare a repertoire of challenging scenarios. When deployed, the adversarial agent can immediately generate difficult test cases without requiring the autonomous system to have encountered every possible scenario during normal operation
2Adaptability or versatility
If more scenarios are simulated to improve training coverage, then the autonomous vehicle can handle more situations, but training time increases
Solution Approach 1:
The patent uses copying by creating a simulated adversarial agent that replicates and extends real-world human behavior patterns found in logged data. Instead of simulating every possible real-world scenario, the adversarial agent copies essential behavioral patterns and generates variations that challenge the autonomous system. This allows comprehensive training coverage to be achieved through synthesized scenarios rather than exhaustive simulation of all possible real-world situations
Solution Approach 2:
The adversarial agent employs parameter changes by systematically varying scenario parameters such as object positions, velocities, accelerations, and behavioral patterns to generate diverse challenging scenarios. By changing key parameters in controlled ways, the system can explore a wide range of difficult situations efficiently, achieving high scenario coverage without linearly increasing training time
3Reliability
If the autonomous vehicle is trained on all possible human behaviors, then it can predict rare events better, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary adversarial agent that mediates between the limited logged real-world data and the autonomous vehicle's prediction system. This adversarial agent acts as a bridge, transforming available data into comprehensive training scenarios that cover rare events without requiring the autonomous system to directly process or model every possible human behavior. The intermediary simplifies the complexity by focusing training on the most challenging and relevant scenarios
4Productivity
If adversarial scenarios are generated to challenge the autonomous vehicle, then training effectiveness improves, but the vehicle may learn aggressive behaviors
Solution Approach 1:
The adversarial agent uses parameter changes within constrained boundaries to generate challenging scenarios. By carefully controlling the range and magnitude of parameter variations (such as limiting maximum acceleration or ensuring scenarios remain physically plausible), the system can create difficult test cases that improve training effectiveness without inducing aggressive or unsafe behaviors in the autonomous vehicle
Data Source
AI summary
A reward determined as part of a machine learning technique, such as reinforcement learning, may be used to control an adversarial agent in a simulation such that a component for controlling motion of the adversarial agent is trained to reduce the reward. Training the adversarial agent component may be subject to one or more constraints and/or may be balanced against one or more additional goals. Additionally or alternatively, the reward may be used to alter scenario data so that the scenario data reduces the reward, allowing the discovery of difficult scenarios and/or prospective events.