Adding an SVO-based Bayesian intention estimator to a DQN controller lowers simulated lane-change collision rates to 7.52% and raises success rates to 92.02%.
Intent-Aware Autonomous Driving: A Case Study on Highway Merging Scenarios
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abstract
In this work, we use the communication of intent as a means to facilitate cooperation between autonomous vehicle agents. Generally speaking, intents can be any reliable information about its future behavior that a vehicle communicates with another vehicle. We implement this as an intent-sharing task atop the merging environment in the simulator of highway-env, which provides a collection of environments for learning decision-making strategies for autonomous vehicles. Under a simple setting between two agents, we carefully investigate how intent-sharing can aid the receiving vehicle in adjusting its behavior in highway merging scenarios.
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cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions
Adding an SVO-based Bayesian intention estimator to a DQN controller lowers simulated lane-change collision rates to 7.52% and raises success rates to 92.02%.