Research On Eco-Safe Speed Advisory Technology At Intersections Based On Vehicular Networks

Improper speed control could cause extra fuel consumption and rear-end collisions in the vicinity of signalized intersections. It is impossible for drivers to attain an optimal driving strategy due to their fuzzy control. Eco-driving and Safe-driving can be attained by using speed advisory systems. In this paper, we have studied the critical technologies on the application of the speed advisory for Eco-driving and Safe-driving at signalized intersections. The research is divided into the following aspects:First, a beacon rate control algorithm is proposed to reduce the tracking error. By analyzing the vehicle trajectory tracking process, it is found that the tracking error is proportional to the moving status of a vehicle, as well as the data delay. Due to the constraint of the wireless communication capacity, the data delay for all the nodes can't be shortened infinitely. The principle of the algorithm design is to shorten the data delay by increasing the beacon rate for vehicles with higher dynamics, due to the reason that these vehicles are prone to cause larger tracking error with the same data delay. Simulation results show that by using the proposed algorithm, the beacon rate of the subject vehicle can adapt to the vehicle dynamics, thus in this way, the tracking error is controlled at a decimeter level. Next, a novel speed advisory model with considering the human driver's behavior in real-world driving is proposed in this paper. The model is established based on the triangle functions with the capability to fit in the close-loop implementation. In order to make the generated speed trajectories easy for drivers to follow, the vehicle coasting is introduced and its mathematical solutions have been derived in this paper. Combined with the coasting process, the CS (Coasting Supplementary) model is established. Simulations have been done on both the open-loop and close-loop implementation of the CS model, as well as the Baseline case. Results comparisons show that the CSCI (CS model with Close-loop Implementation) has the ability to adapt to the changing situations, and can save fuel up to 20%, as well as shorten the travel time compared with the Baseline case. Third, a robust rear-end collision warning model is devised to address the problem of high false alarm rates and high missing alarm rates in DSRC-based rear-end collision warning systems without using expensive high-end devices. Simulations have shown that high rates (up to 56%) of missing alarms occur in the Vehicle Kinematics (VK) model, as well as false alarms (most of which exceed 70%) in the Vehicle Kinematics with Maximum Compensation (VK-MC) model. Pertaining to these rates, a novel model based on neural network (NN) approach is implemented. Through training and validation, the NN model is able to provide emergency warnings with an improved performance of false alarm probability under 20%, and the missing alarm probability under 10% for all test cases. Last, we have coupled the eco-speed advisory model with the rear-end collision warning model. The couple method is divided into tight couple and loose couple. Simulation results show that the tight couple model is better than the loose one.