A Critical Evaluation of Eco-Driving Strategies for Connected Autonomous Electric Vehicles at Signalized Intersections

Signalized intersections are significant spots of energy consumption because of frequent stop-and-go behavior. Eco-driving aims to reduce energy usage by optimizing driving behavior. Researchers have reviewed optimization-based method while lack of them reviewed the learning-based approaches. This work critically reviewed two different types of approach. In addition, one well-known rule-based car-following model and two state-of-the-art optimization-based and learning-based methods are selected to test in a signalized intersections environment with the metrics of energy consumption, travelling time and algorithm execution time. The experiment results show that the travelling time of three algorithms are similar, while the energy consumption of the learning-based method and optimization-based method are 30.72% and 51.82% less than that of the rule-based method respectively. However, due to algorithm execution time, the optimization-based method is not suitable to be used in real-time

Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development

Modelling Eco-Driving Support System for Microscopic Traffic Simulation

Microscopic traffic simulation is an ideal tool for investigating the network level impacts of eco-driving in different networks and traffic conditions, under varying penetration rates and driver compliance rates. The reliability of the traffic simulation results however rely on the accurate representation of the simulation of the driver support system and the response of the driver to the eco-driving advice, as well as on a realistic modelling and calibration of the driver’s behaviour. The state-of-the-art microscopic traffic simulation models however exclude detailed modelling of the driver response to eco-driver support systems. This paper fills in this research gap by presenting a framework for extending state-of-the-art traffic simulation models with sub models for drivers’ compliance to advice from an advisory eco-driving support systems. The developed simulation framework includes among others a model of driver’s compliance with the advice given by the system, a gear shifting model and a simplified model for estimating vehicles maximum possible acceleration. Data from field operational tests with a full advisory eco-driving system developed within the ecoDriver project was used to calibrate the developed compliance models. A set of verification simulations used to illustrate the effect of the combination of the ecoDriver system and drivers’ compliance to the advices are also presented

Assessing the impact of closely-spaced intersections on traffic operations and pollutant emissions on a corridor level

Traffic lights or roundabouts along corridors are usually installed to address location-specific operational needs. An understanding of the impacts on traffic regarding to highly-congested closely-spaced intersections has not been fully addressed. Accordingly, consideration should be given to how these specific segments affect corridor performance as a whole. One mixed roundabout/traffic light/stop-controlled junctions corridor was evaluated with the microscopic traffic model (VISSIM) and emissions methodology (Vehicle Specific Power – VSP). The analysis was focused on two major intersections of the corridor, a roundabout and a traffic light spaced lower than 170 m apart under different traffic demand levels. The traffic data and corridor geometry were coded into VISSIM and compared with an alternative scenario where the traffic light was replaced by a single-lane roundabout. This research also tested a method to improve corridor performance and emissions by examining the integrated effect of the spacing between these intersections on traffic delay and vehicular emissions (carbon dioxide, monoxide carbon, nitrogen oxides, and hydrocarbons). The Fast Non-Dominated Sorting Genetic Algorithm (NSGA-II) was used to find the optimal spacing for these intersections. The analysis showed that the roundabout could achieve lower queue length (∼64%) and emissions (16–27%, depending on the pollutant) than the traffic light. The results also suggested that 200 m of spacing using the best traffic control would provide a moderate advantage in traffic operations and emissions as compared with the existing spacing

Providing Real-time Driver Advisories in Connected Vehicles: A Data-Driven Approach Supported by Field Experimentation

Approximately 94\% of the annual transportation crashes in the U.S. involve driver errors and violations contributing to the $1 Trillion losses in the economy. Recent V2X communication technologies enabled by Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) can provide cost-effective solutions for many of these transportation safety applications and help reduce crashes up to 85%. This research aims towards two primary goals. First, understanding the feasibility of deploying V2V-based safety critical applications under the constraints of limited communication ranges and adverse roadway conditions. Second, to develop a prototype application for providing real-time advisories for hazardous driving behaviors and to notify neighboring vehicles using available wireless communication platform. Towards accomplishing the first goal, we have developed a mathematical model to quantify V2V communication parameters and constraints pertaining to a DSRC-based “Safe pass advisory” application and validated the theoretical model using field experiments by measuring the communication ranges between two oncoming vehicles. We also investigated the impacts of varying altitudes, vehicle-interior obstacles, and OBU placement on V2V communication reliability and its implications. Along the direction of the second goal, we derived a data-driven model to characterize the acceleration/deceleration profile of a regular passenger vehicle with respect to speed and throttle position. As a proof of concept demonstration, we implemented an IoT-based communication architecture for disseminating the hazardous driving alerts to vulnerable drivers through cellular and/or V2X communication infrastructure