John Spring

Bio: John Spring is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Cooperative Adaptive Cruise Control & Cruise control. The author has an hindex of 6, co-authored 13 publications receiving 784 citations.

Cooperative Adaptive Cruise Control in Real Traffic Situations

Abstract: Intelligent vehicle cooperation based on reliable communication systems contributes not only to reducing traffic accidents but also to improving traffic flow. Adaptive cruise control (ACC) systems can gain enhanced performance by adding vehicle-vehicle wireless communication to provide additional information to augment range sensor data, leading to cooperative ACC (CACC). This paper presents the design, development, implementation, and testing of a CACC system. It consists of two controllers, one to manage the approaching maneuver to the leading vehicle and the other to regulate car-following once the vehicle joins the platoon. The system has been implemented on four production Infiniti M56s vehicles, and this paper details the results of experiments to validate the performance of the controller and its improvements with respect to the commercially available ACC system.

Cooperative Adaptive Cruise Control: Testing Drivers' Choices of Following Distances

Abstract: A Cooperative Adaptive Cruise Control (CACC) system has been developed by adding a wireless vehicle-vehicle communication system and new control logic to an existing commercially available adaptive cruise control (ACC) system. The CACC is intended to enhance the vehicle-following capabilities of ACC so that drivers will be comfortable using it at shorter vehicle-following gaps than ACC. This can offer a significant opportunity to increase traffic flow density and efficiency without compromising safety or expanding roadway infrastructure. This report describes the design and implementation of the CACC system on two Infiniti FX-45 test vehicles, as well as the data acquisition system that has been installed to measure how drivers use the system, so that the impacts of such a system on highway traffic flow capacity and stability can be estimated. The results of quantitative performance testing of the CACC on a test track are presented, followed by the experimental protocol for on-road testing with human subjects. Finally, the results from the field testing by 16 naive drivers are presented to show the user acceptance and quantitative measurements of how these drivers used the ACC and CACC systems, and how these systems affected their choice of car following gap.

A First Investigation of Truck Drivers’ On-the-Road Experience Using Cooperative Adaptive Cruise Control

Abstract: Author(s): Yang, Shiyan; Shladover, Steven E.; Lu, Xiao-Yun; Spring, John; Nelson, David; Ramezani, Hani

Onboard monitoring and reporting for commercial motor vehicle safety

Abstract: This Final Report describes the process and product from the project, Onboard Monitoring and Reporting for Commercial Motor Vehicle Safety (OBMS), in which a prototypical suite of hardware and software on a class 8 truck was developed and tested. The OBMS suite allows for online measurement of a set of driving characteristics which are indicators of unsafe driving behavior. These characteristics included speed, following distance, lane-keeping performance, safety belt use, and the use of turn signals. Feedback could be provided to the drivers, either directly via real-time feedback or through carrier management, to allow drivers to significantly improve their safety performance. Commercial fleets would pioneer this concept because they have the resources and organizational structure to provide feedback and training to professional drivers. This concept differs from commercial onboard devices in that it is an ensemble set of instruments with a safety focus and different feedback modalities. It is comprehensive in that it addresses crash causes and provides "corrective" feedback in real-time and/or post trip feedback, depending on the particular subsystem(s) which are activated. In essence, the objective is to improve driver safety behavior. Thus, it does not explicitly address fleet management or other non-safety operations (for example, vehicle location).

SafeTrip 21 initiative : networked traveler foresighted driving field experiment, final report.

Abstract: This report describes the SafeTrip-21, Networked Traveler Foresighted Driving Field Experiment conducted as part of the US DOT’s SafeTrip-21 initiative. This experiment developed and evaluated an Advanced Driver Assistance System providing situational awareness alerts regarding “Slow Traffic Ahead” when driving on a freeway. The Networked Traveler system detects slow traffic or queues at several thousand locations in the San Francisco Bay area, monitors the locations and speeds of its test subjects as they drive, and determines if the driver is approaching the slow traffic fast enough to warrant an alert. If so, the system alerts the driver through an auditory interface. The desired outcome is a foresighted reduction in speed, resulting in a smoother overall transition into the oncoming traffic queue. The system aims to reduce the likelihood of end-of-queue crashes (a subset of rear-end crashes) on freeways. The hypothesis is tested by computing measures representing the Root Mean Square (RMS) Error of Speed, Peak Deceleration Rate, Mean Deceleration Rate, Deceleration Due to Braking, Pre-Braking Deceleration, and Time before the start of braking. Among these, the RMS Error of Speed across all subjects most clearly confirms the test hypothesis that enhanced situational awareness results in smoother driving and a lower crash risk.

A Review of Motion Planning Techniques for Automated Vehicles

Abstract: Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

Impacts of cooperative adaptive cruise control on freeway traffic flow

Abstract: This study used microscopic simulation to estimate the effect on highway capacity of varying market penetrations of vehicles with adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC). Because the simulation used the distribution of time gap settings that drivers from the general public used in a real field experiment, this study was the first on the effects of ACC and CACC on traffic to be based on real data on driver usage of these types of controls. The results showed that the use of ACC was unlikely to change lane capacity significantly. However, CACC was able to increase capacity greatly after its market penetration reached moderate to high percentages. The capacity increase could be accelerated by equipping non-ACC vehicles with vehicle awareness devices so that they could serve as the lead vehicles for CACC vehicles.

Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

Abstract: Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: (1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; (2) strings of consecutive ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and (3) strings of consecutive CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios.

Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

Abstract: Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.

A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC)

Abstract: Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.