Real Time Planning Algorithm of Automatic Train Driving Based on Global Optimization

Abstract

Objective The existing target speed planning algorithm for the automatic driving of urban rail transit train cannot handle in real time the temporary change of the line speed limit in case of emergency due to the large computational load and long computation time. With regard to the problem, a real-time target speed planning algorithm based on global optimization is proposed to generate the speed planning curve in real time. Method Based on the conditions of train current position, current speed and speed limit of the forward line, the speed planning curve is firstly generated in the shortest time through point by point calculation. Then the traction and braking levels are being adjusted to keep the train running at uniform acceleration or deceleration, optimizing the comfort index of the train running. Next, the cruising speed in the maximum speed limit section is adjusted to reduce unnecessary traction and braking time, optimizing the energy consumption index of the train running. Finally, the train speed planning curve is output. Result & Conclusion The simulation results show that the real-time speed planning curve generated by the proposed algorithm satisfies the basic constraints of safety, punctuality and accurate parking. Compared with the traditional algorithm, it improves the comfort level and reduces the energy consumption during train operation. Meanwhile, the proposed algorithm can effectively handle the temporary change of the line speed limit in emergency and optimize several operation indicators.

Keywords

• urban rail transit
• automatic driving
• real-time speed planning
• global optimization