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It is crucial to understand the surrounding cars with respect to the road context and interact with them harmoniously for the success of autonomous cars used in the mixed urban traffic. In this paper, a vision-based approach is proposed to implement the humanlike autonomous driving function along a predefined lane-level route in the complex urban environment with daily traffic. At first, the surrounding cars are located in the lane level by a deep neural network based detector with a low-cost lane graph. Subsequently, a Bayesian network is employed to classify the detected cars into six categories based on their states of operation, i.e., leader car, parked car, tail-end car, exiting car, merging car, and other car. Finally, a hybrid potential map, consisting of a trajectory-induction potential and a risk-prevention potential, is constructed for each of the cars according to their categories, which will be combined to be used for …