Research on eco-driving strategy at intersection based on vehicle infrastructure cooperative system

Abstract

An eco-driving strategy is established in this article which includes four parts, namely, initial judging model, speed prediction model based on back-propagation neural network, MATLAB curve fitting, and integral. First, based on vehicle infrastructure cooperative systems, the initial judging model is instructed and vehicle road test is conducted. Then, a speed forecast model based on back-propagation neural network had been set up using test data obtained in the previous step. Next ecodriving strategy had been specified using curve fitting based on the forecast speed data and integral in MATLAB. Finally, a verification test had been done using VISSIM simulation tool. The conclusion of the test showed that using eco-driving strategy was conducive to decrease fuel consumption efficiently when driving at intersection. This article provided a specific case in application of vehicle infrastructure cooperative system to study on fuel consumption and emission in city traffic.

Research on a Speed Guidance Strategy for Mine Vehicles in Three-Fork Roadways Based on Vehicle–Road Coordination

“…This design is not only an important node for traffic flow convergence, steering, and diversion during the transportation of underground roadways but also the key to ensuring smooth underground traffic [2]. When a coal mine underground mining vehicle cannot obtain countdown information from the three-fork roadway signal light intersection in real time during driving, the lack of reasonable speed suggestions causes the mining vehicle to frequently accelerate, decelerate, and stop [3], causing traffic bottlenecks [4][5][6], resulting in the three-fork roadway intersection becoming the key area of coal mine underground transportation network congestion [7][8][9], which increases the hidden safety hazards of coal mine underground mining vehicles and reduces transportation efficiency. The traditional underground vehicle control model of a coal mine does not consider the timing of signal lights and does not organically combine the intersection signal duration with the real-time status of the vehicle, which has serious safety risks [10,11].…”

Enhanced speed advice for connected vehicles in the proximity of signalized intersections

“…Efforts were also placed for the development of dynamic eco-driving models that can estimate energy and traffic efficient speed advice for CV platoons in the proximity of signalized intersections [7,43,52,54,59]. Recently, artificial intelligence has been also used for applying dynamic eco-driving control in the proximity of signalized intersections [33,58].…”