RAMRL: Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning
Despite the success of AI-enabled onboard perception, on-ramp merging has been one of
the main challenges for autonomous driving. Due to limited sensing range of onboard
sensors, a merging vehicle can hardly observe main road conditions and merge properly. By
leveraging the wireless communications between connected and automated vehicles
(CAVs), a merging CAV has potential to proactively obtain the intentions of nearby vehicles.
However, CAVs can be prone to inaccurate observations, such as the noisy basic safety …
the main challenges for autonomous driving. Due to limited sensing range of onboard
sensors, a merging vehicle can hardly observe main road conditions and merge properly. By
leveraging the wireless communications between connected and automated vehicles
(CAVs), a merging CAV has potential to proactively obtain the intentions of nearby vehicles.
However, CAVs can be prone to inaccurate observations, such as the noisy basic safety …
Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning
Despite the success of AI-enabled onboard perception, on-ramp merging has been one of
the main challenges for autonomous driving. Due to limited sensing range of onboard
sensors, a merging vehicle can hardly observe main road conditions and merge properly. By
leveraging the wireless communications between connected and automated vehicles
(CAVs), a merging CAV has potential to proactively obtain the intentions of nearby vehicles.
However, CAVs can be prone to inaccurate observations, such as the noisy basic safety …
the main challenges for autonomous driving. Due to limited sensing range of onboard
sensors, a merging vehicle can hardly observe main road conditions and merge properly. By
leveraging the wireless communications between connected and automated vehicles
(CAVs), a merging CAV has potential to proactively obtain the intentions of nearby vehicles.
However, CAVs can be prone to inaccurate observations, such as the noisy basic safety …
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