Field implementation and testing of an automated eco-cooperative adaptive cruise control system in the vicinity of signalized intersections

https://doi.org/10.1016/j.trd.2018.11.019Get rights and content

Highlights

  • The automated Eco-CACC system could achieve fuel savings of 31% on average.

  • The manual Eco-CACC system could achieve fuel savings of 13% on average.

  • The Eco-CACC system also reduced the travel time by 8.5% approximately.

  • The Eco-CACC system reduced the percentage of time idling at intersection to zero.

Abstract

This paper field implements and conducts a unique controlled field experiment designed to evaluate an Eco-Cooperative Adaptive Cruise Control (Eco-CACC) system that computes fuel-efficient trajectories that are either recommended to drivers or implemented within a traditional ACC system. The controlled field experiment included three different scenarios: normal driving, driving with a speed advisory (manual Eco-CACC system), and automated Eco-CACC. The controlled field experiment was conducted for four red indication offset values (with two repetitions for each offset) randomly delivered to drivers traveling along an uphill and downhill approach on the Smart Road test facility. In total, 1536 trips were conducted by 32 different participants between the ages of 18 and 30 with an equal number of males and females. The collected data were compared with regard to fuel economy and travel time over a fixed distance starting upstream and ending downstream of the intersection (from 820 feet [250 m] upstream of the intersection to 590 feet [180 m] downstream for a total length of 1410 feet [430 m]). The results demonstrate that the proposed Eco-CACC system reduces fuel consumption levels significantly, especially when driving downhill. Specifically, the results indicate that the automated scenario could achieve fuel and travel time savings of 31% and 9% on average, respectively. These results also demonstrate that automatic longitudinal control produces significant benefits over human control (an approximately19% reduction in fuel consumption).

Introduction

The transportation sector is the second largest contributor to an increase in fuel consumption and emission levels in the United States (Agency, 2010). A rise in vehicle travel miles and, consequently, traffic congestion has led to greater fuel consumption and pollution. One solution, the concept of eco-driving, has been studied widely as a way to mitigate the environmental impacts of transportation (Rafael, 2006).

Eco-driving is a driving style that is considered to be both economical and ecologically beneficial. It has been defined by several researchers as the smooth driving resulting from adjustments in driver behavior (De Vlieger et al., 2000). Consequently, researchers have provided tips for drivers (Ando et al., 2010) on how to effectively minimize fuel consumption and emission levels. These tips are:

  • Avoid hard braking, sudden acceleration/deceleration, and idling.

  • Limit air conditioner use.

  • Do not warm engine.

  • Upshift early.

  • Maintain steady speed.

  • Look ahead while driving.

  • Limit starting speed in the first 5 s to 12.4 mi/h (20 km/h).

In addition, recent research has shown how onboard driver assistance systems can be installed in vehicles to help drivers successfully follow eco-driving principles (Ando et al., 2010, Vagg, 2013). These devices can deliver “static” advice to drivers such as “accelerate slowly,” “avoid hard braking,” and “reduce high speed.” However, the impact of static advice on fuel consumption and emission levels is limited.

By taking advantage of real-time traffic sensing and infrastructure information, onboard driver assistance systems can deliver “dynamic” advice to drivers that can help them drive smoothly and efficiently. In particular, establishing communication between the vehicle and the traffic signal controller is a powerful tool to make driver behavior smoother, thus reducing vehicle fuel consumption levels. Various messages can be exchanged between the vehicle and the traffic signal controller to provide better advice to drivers. Among these messages is the Signal Phasing and Timing (SPaT) message, which can provide approaching vehicles with valuable advance information about upcoming traffic signal timing changes. This information could help drivers make early decisions about whether to proceed or stop safely and smoothly.

With the help of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, algorithms are being developed that utilize SPaT data together with queue information to optimize vehicle trajectories in the vicinity of signalized intersections (Mandava et al., 2009, Asadi and Vahidi, 2011, Barth, et al., 2011, Sun, 2013). In other words, vehicles can communicate with traffic signal controllers, receive SPaT information, and adjust their speeds accordingly and automatically. Several Eco-Speed control (ESC) algorithms have been introduced to provide an optimal trajectory through signalized intersections. However, none explicitly attempt to minimize vehicle fuel consumption levels. In addition, most of the proposed algorithms have been developed and tested in traffic simulation environments where recommended speeds are enforced and many issues, such as the delay in the system and human-vehicle interaction, are not considered.

Kamalanathsharma (2014) developed a new ESC algorithm named Eco-Cooperative Adaptive Cruise Control (Eco-CACC). The Eco-CACC system computes and recommends real-time, fuel-efficient speeds using V2V- and V2I-communicated data within the vicinity of the traffic signalized intersection. The objective function of the Eco-CACC system is the explicit minimization of the total fuel consumed to travel from some distance upstream of the intersection to a distance downstream of the intersection. The Eco-CACC system was tested and evaluated in a simulation environment. A reduction in fuel consumption of over 30% was achieved. In a preliminary field test, we addressed the implementation issues and challenges of the field application of the Eco-CACC system (Chen, 2016) and showed that the Eco-CACC system is practical, applicable and very promising in terms of fuel consumption and travel time savings. Also, In Almannaa (2017), we tested the Eco-CACC system (with only human-vehicle interaction) in a real environment and found it could save up to 19% of fuel consumption within the vicinity of signalized intersections. Furthermore, we studied the queue effects, multiple intersections, and interaction of Eco-CACC and non-Eco-CACC vehicles in a simulation environment (Yang et al., 2017, Yang and Almutairi, 2017).

In this paper, we extend our work by conducting a more extensive controlled field experiment using 32 participants on the Virginia Smart Road test facility at the Virginia Tech Transportation Institute (VTTI) with the goal of evaluating the Eco-CACC system with regard to fuel consumption and travel time savings. This experiment is mainly testing the human-vehicle interaction (driver perception/reaction delay) under controlled circumstances as well as considering other technical issues: communication latency, system malfunction, and data collection error. Three different scenarios were considered: normal driving, driving with recommended speed information provided to drivers (a manual Eco-CACC system), and finally automated driving (an automated Eco-CACC system). Previous studies have shown that visual messages are less efficient and more distracting for drivers compared to audio messages (Jamson et al., 2015, Horberry, 2006, Tang, 2016). Consequently, the computed speeds in the manual Eco-CACC system were delivered to drivers as audio messages.

The following sections provide a brief literature review; a brief description of the algorithm used; the experimental design used to gather the data; field data analysis and findings; and finally the conclusions of the research and recommendations for future work.

Section snippets

Background

Researchers have recently realized the significant increase in vehicle fuel consumption levels that can be attained in the vicinity of signalized intersections (Courage and Parapar, 1975). When coming close to a signalized intersection, drivers are unaware of exactly when the traffic signal indication will change. Consequently, drivers may have to accelerate/decelerate aggressively to respond to these changes. This results in non-smooth driving behavior. Non-smooth driving (e.g., hard barking,

Methodology

The control algorithm in the Eco-CACC system was developed in the previous work (Chen, 2016, Chen, et al., 2017, Rakha, et al., 2016) to provide a fuel-optimized speed profile both upstream and downstream of a signalized intersection. The objective function of the proposed algorithm is the explicit minimization of the total fuel consumed to travel from some distance upstream of the intersection to a distance downstream of the intersection. In addition, various constraints are constructed using

Field data analysis

Vehicle trip information (e.g., the current and recommended speed, distance to stop line, time, and SPaT information) was recorded each decisecond. Only the data in the Eco-CACC range (820 feet [250 m] upstream to 590 feet [180 m] downstream) were extracted and analyzed. In total, 1536 trips were recorded for 32 participants.

The in-field data results show that the fuel consumption measurements were not reliable as they do not capture the sudden changes in speeds and, also, give a value of zero

Experiment

The results show that both the manual and automated Eco-CACC systems always improved the vehicle trajectory for all treatment combinations. In particular, they decreased acceleration and deceleration maneuvers and provided a smooth speed profile. This can be seen in Fig. 11, in which an example vehicle speed profile and trajectory are compared for the three scenarios for each red indication offset value. Note that zero marks time when the test vehicle enters the range of the Eco-CACC system. In

Conclusions and recommendations for future research

The research presented in this paper developed, field implemented, field tested, and quantified the benefits of an Eco-CACC system that computes a vehicle’s fuel-optimal trajectory in real-time using SPaT information received from a downstream traffic signal controller. A controlled field experiment involving 32 participants was conducted on the Smart Road test facility at VTTI. The experiment included three scenarios: normal driving, driving with the manual Eco-CACC system enabled, and driving

Acknowledgements

This research effort was jointly funded by the TranLIVE University Transportation Center, NPRP Grant # 5-1272-1-214 from the Qatar National Research Fund (a member of The Qatar Foundation), and the University Equity and Mobility Center (UMEC). The authors acknowledge the help of researchers from the hardware group at VTTI for their assistance in developing the hardware and software environment.

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