  This paper presents a novel approach to address the challenging problem of
autonomous on-ramp merging, where a self-driving vehicle needs to seamlessly
integrate into a flow of vehicles on a multi-lane highway. We introduce the
Lane-keeping, Lane-changing with Latent-state Inference and Safety Controller
(L3IS) agent, designed to perform the on-ramp merging task safely without
comprehensive knowledge about surrounding vehicles' intents or driving styles.
We also present an augmentation of this agent called AL3IS that accounts for
observation delays, allowing the agent to make more robust decisions in
real-world environments with vehicle-to-vehicle (V2V) communication delays. By
modeling the unobservable aspects of the environment through latent states,
such as other drivers' intents, our approach enhances the agent's ability to
adapt to dynamic traffic conditions, optimize merging maneuvers, and ensure
safe interactions with other vehicles. We demonstrate the effectiveness of our
method through extensive simulations generated from real traffic data and
compare its performance with existing approaches. L3IS shows a 99.90% success
rate in a challenging on-ramp merging case generated from the real US Highway
101 data. We further perform a sensitivity analysis on AL3IS to evaluate its
robustness against varying observation delays, which demonstrates an acceptable
performance of 93.84% success rate in 1-second V2V communication delay.
