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Given the current point-to-point navigation capabilities of autonomousvehicles, researchers are looking into complex service requests that requirethe vehicles to visit multiple points of interest. In this paper, we develop alayered planning framework, called GLAD, for complex service requests inautonomous urban driving. There are three layers for service-level,behavior-level, and motion-level planning. The layered framework is unique inits tight coupling, where the different layers communicate user preferences,safety estimates, and motion costs for system optimization. GLAD is visuallygrounded by perceptual learning from a dataset of 13.8k instances collectedfrom driving behaviors. GLAD enables autonomous vehicles to efficiently andsafely fulfill complex service requests. Experimental results from abstract andfull simulation show that our system outperforms a few competitive baselinesfrom the literature.

GLAD: Grounded Layered Autonomous Driving for Complex Service Tasks