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The decision-making model of merging behavior is one of the key technologies of unmanned vehicles. In order to solve the problem of unmanned vehicles’ merging decision making, this paper presents a merging strategy based on Least squares Policy Iteration (LSPI) algorithm, and selects the basis function which includes reciprocal of TTC, relative distance and relative speed to represent state space and discretizes action space. This study synthetically takes consideration o safety, the success of the task, the merging efficiency and comfort in setting reward function, compares the Q-learning with LSPI algorithm, and verifies its adaptability by using NGSIM data. The algorithm can ultimately achieve a success rate of 86%. This research can provide theoretic support and technical basis for the merging decision-making of unmanned vehicles.

Car-following decision-making is critical to complete driving mission for autonomous vehicles under complex and dynamic urban environment. The complex and dynamic information have a great influence on the autonomous vehicle's car-following decision. This paper proposes to use the Rough Set theory to abstract the car-following rules to support the decision-making of autonomous vehicles under the complex and dynamic urban environment. Firstly, a virtual urban traffic environment is built by PreScan (a simulation environment for developing advanced driver assistant system). The vehicle dynamics is simulated using 6-DOF dynamic model and the experimental data were acquiring based on MATLAB/Simulink. Secondly, the Rough Set theory is proposed to reduce the influence of weak interdependency data, and extract the driver's decision rules. Finally, the result is that: the subject car will slow down or break when the relative distance between the subject vehicle and the leading vehicle D is less than 7.7m, drive at a constant speed when D is under 10.2-12.7m, and accelerate when D is beyond 12.7m. It shows that the Rough Set can extract the driver's decision rules from the combining multiple sensors data and related information providing a theoretical basis for the car-following decision-making under complex and dynamic environment.

This paper tries to make self-driving vehicles have human drivers' common sense and intuitive decision-making ability. Human drivers often make decisions according to not only what they see, but also their predictions based on experiences and reasoning results. The authors propose a systematical intuitive decision-making for self-driving vehicles. The method combines similarity matching, online learning mechanism and prediction together. Similarity matching can make a decision based on previous learned knowledge, while online learning can enrich the knowledge database, and prediction can make the system have reasoning common sense to produce decisions in unfamiliar and incomplete traffic scenarios. Basically, intuitive decision-making can produce a decision quickly without long-time reasoning computation. A simple test example tested the proposed method.