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Extensive studies have been conducted on unmanned vehicle recently due to its bright prospect. However, few of them have studied the technology of driverless bus. In this paper, a path planning and navigation control system is designed for 12-m length driverless electric bus. The global path planning is adopted by the Dijkstra algorithm based on the analysis tool of ArcGIS. For local planning, a triple-layer finite-state machine is proposed to plan the driving maneuver, and it reduces computational complexity and improves driving effectiveness. Then, optimal trajectory is generated by a curve fitting technique based on path planning and driving maneuver, which takes full account of the safety and kinetics for driverless bus. In addition, we address the speed planning by expert rules to imitate an experienced driver. Finally, the trajectory tracking controller is devised by an adaptive rolling window. Moreover, a heading error compensation model and a self-adaptive strategy of controller parameters are set up to improve the control precision. Finally, simulation by TruckSim and scenes experiments verify the validity and practicability of our proposed path planning and navigation control system for driverless bus.

Erythritol as a phase change material (PCM) has the advantage of extremely high latent heat, excellent thermal stability. However, its low thermal conductivity and easy leakage greatly limit its practical application, so the development of novel shape-stabilized PCM with high thermal conductivity is of great significance. In this paper, thermal conductivity, melting point and adsorption properties of hierarchical porous diamond/erythritol composite PCM were calculated using molecular dynamics simulation. When the load of erythritol is 12.91 wt%, the thermal conductivity can reach 2.06 W ⋅m−1K−1, which is 207% higher than that of pure erythritol. The phonon vibration of erythritol plays an important role in the composite. Erythritol enhanced the mass transfer and shared part of the heat flux, which acted as an auxiliary heat channel. With the increase of load, the melting point increased and tended to bulk erythritol. The flexibility of erythritol in confined space was better, resulting in lower melting point than that in free space. The advantage of hierarchical porous diamond was verified by calculating centroid position of erythritol and interaction energy between host and guest. Significantly, the meso pore is conducive to heat and mass transfer, and the micro pore has stronger interaction with the core material.

A framework of path planning for autonomous electric bus is presented. ArcGIS platform is utilized for map-building and global path planning. Firstly, a high-precision map is built based on GPS in ArcGIS for global planning. Then the global optimal path is obtained by network analysis tool in ArcGIS. To facilitate local planning, WGS-84 coordinates in the map are converted to local coordinates. Secondly, a double-layer finite state machine (FSM) is devised to plan driving behavior under different driving scenarios, such as structured driving, lane changing, turning, and so on. Besides, local optimal trajectory is generated by cubic polynomial, which takes full account of the safety and kinetics of the electric bus. Finally, the simulation results show that the framework is reliable and feasible for driving behavior planning and trajectory generation. Furthermore, its validity is proven with an autonomous bus platform 12 m in length.

The autonomous overtaking maneuver is a valuable technology in unmanned vehicle field. However, overtaking is always perplexed by its security and time cost. Now, an autonomous overtaking decision making method based on deep Q-learning network is proposed in this paper, which employs a deep neural network(DNN) to learn Q function from action chosen to state transition. Based on the trained DNN, appropriate action is adopted in different environments for higher reward state. A series of experiments are performed to verify the effectiveness and robustness of our proposed approach for overtaking decision making based on deep Q-learning method. The results support that our approach achieves better security and lower time cost compared with traditional reinforcement learning methods.