Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic

Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.

Heavy-duty truck platooning on hilly terrain highways: Methods for assessment and improvement

Platooning heavy-duty trucks is a proven method to reduce fuel consumption on flat ground, but a significant portion of the U.S. highway system covers hilly terrain. The effort described in this paper uses experimentally gathered single truck data from a route with hilly terrain and an experimentally-validated two-truck platoon simulation framework to analyze control methods for effective platooning on hilly terrain. Specifically, this effort investigates two platoon control aspects: (1) the lead truck’s vehicle speed control and (2) the platoon’s transmission shifting algorithm. Three different types of lead truck speed control strategies are analyzed using the validated platoon model. Two are commercially available cruise control strategies – conventional constant set speed cruise control (CCC) and flexible set speed cruise control (FCC). The third lead truck speed control strategy was developed by the authors in this paper. It uses look-ahead grade information for an entire route to create an energy-optimal speed profile for the lead truck which is called long-horizon predictive cruise control (LHPCC). Then, a two-truck platoon transmission shifting strategy that coordinates the shift events – Simultaneous Shifting (SS) – is introduced and compared to a commercially available shifting strategy using the validated platoon model. This shifting strategy demonstrates further improvements in the platoon performance by improving the platoon gap control. A summary of these simulations demonstrates that the performance of the platoon can be improved by three methods: adding speed flexibility to the lead truck speed control method, using look-ahead road grade information to generate energy-optimal speed targets for the lead truck, and coordinating the timing of the transmission shifts for each truck in the platoon.

A distributed model predictive control approach to cooperative adaptive cruise control of the heterogeneous platoon

This paper presents a distributed model predictive control algorithm to solve the cruise control problem of a heterogeneous platoon. Each following vehicle in the platoon can use the communication equipment to receive the information of the leading vehicle and its preceding adjacent one. The vehicles in the platoon are dynamically decoupled and have different dynamic parameters. Each vehicle solves a local optimal control problem independently. The cost function of each vehicle’s local optimal control algorithm is designed with traceability as the control objective, and its asymptotic stability is guaranteed by using the terminal constraint method. In addition, the timestamps of all vehicles in the platoon are synchronous, which means that in each sampling period, a specific vehicle in the platoon cannot obtain the solution results of other vehicles’ local optimal control problems at the current sampling moment. Under this restriction, the constraints that each vehicle needs to meet to realize the platoon’s string stability are also designed. Finally, the simulation results show the effectiveness of the algorithm.