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We present a novel autonomous driving system which uses the road contextual information and intentions of other road users for urban driving. Unlike highways, urban environments require the drivers to follow traffic signs and signals while using their best judgment for anomalous situations. In such scenarios, a self-driving car needs to understand and take into account the uncertainties in the environment to plan and decide its action accordingly. Our planner models the intentions of the surrounding vehicles leveraging a neural network, and integrates the road contextual information to reduce its environment uncertainties and also speed up the decision making process. We validate our planner in simulation and in a real urban environment. Our experimental results show that integrating intention inference and road contextual information for prediction, planning and decision making help improve safety and …