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This paper proposes a prediction method for vehicle-to-pedestrian collision avoidance, which learns and then predicts pedestrian behaviors as their motion instances are being observed. During learning, known trajectories are clustered to form Motion Patterns (MP), which become knowledge a priori to a multi-level prediction model that predicts long-term or short-term pedestrian behaviors. Simulation results show that it works well in a complex structured environment and the prediction is consistent with actual behaviors.

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This paper proposes a behavior prediction method for navigation application in dynamically changing environments, which predicts obstacle behaviors based on learned Obstacle Motion Patterns (OMP) from observed obstacle motion trajectories. A multi-level prediction model is then proposed that predicts longterm or short-term obstacle behaviors. Simulation results show that it works well in a complex environment and the prediction is consistent with actual behaviors.

In this paper, we propose a new method for sobing the reinforcement learning problem in a dynamically changing environment, as inivehicle navigatiop, in which the Markov Decision Process used in traditional reinforcement learning is modified so that the response of the environment is taken into consideratiin for determining the agent's next state. This i s achieved by changing the action-value function to handle three parameters at a time, namely, the current state, action taken by the agent, and action taken by the environment. As it considers the actions by the agent and environment, it is termed "Double Action". Based on the Q-learning method, the proposed method is implemented and the update rule is modified to handle all of the three parameters. Preliminary results show that the proposed method has the sum of rewards (negative) 89.5% less than that of the traditional method. Apart form that, our new method also has the total number of collisions and mean steps used in one episode 89.5% and 15.5% lower than that of the traditional method respectively.