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Imitation learning methods may shine in controlled environments, but they falter dramatically in real-world urban settings, revealing a stark trade-off in motion planning resilience.
G2DP achieves a remarkable 7.2-point improvement in reactive driving performance by integrating dense environmental constraints directly into the planning process.
Human-in-the-loop RL can optimize semantic communication in Open RAN to meet stringent latency requirements, opening the door for practical deployment in safety-critical applications.
