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The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.

A 2-D gas-particle two-phase flow model has been developed to study the flow characteristics in a single channel of a honeycomb ceramic diesel particulate filter. A particle source in cell (PSIC) algorithm is used to calculate the gas-particle two-phase flow. Firstly, the gas-phase flow field alone (without taking into account of the particle-phase) is solved for estimation of gas velocity and pressure fields in the Euler coordinate. Secondly, the particle-phase is added in and particles tracked down in the Lagrange coordinate. Thirdly, the particle source which acts on the gas-phase cell is calculated and added to the gas-phase equations. Fourthly, the gas-phase equations with the particle source are solved again. Lastly, the above process is iterated until the flow field is convergent. Taking the above-mentioned approach, the gas-particle two-phase flow characteristic is simulated using FLUENT. The simulation results are in good agreement with experiment data.