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This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

With the current drive of automotive and technology companies towards producing vehicles with higher levels of autonomy, it is inevitable that there will be an increasing number of SAE level L4-L5 autonomous vehicles (AVs) on roadways in the near future. Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation.

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies, such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication and automated driving capability. As the world of transportation gets more and more connected through these technologies, the need to implement algorithms with V2I capability is amplified. In this paper, an algorithm called Pass at Green, utilizing V2I and vehicle longitudinal automation to modify the speed profile of a mid-size generic vehicle to decrease fuel consumption has been studied. Pass at Green (PaG) uses Signal Phase and Timing (SPaT) information acquired from upcoming traffic lights, which are the current phase of the upcoming traffic light and remaining time that the phase stays active.

This paper is on the design of a steer-by-wire system for a light commercial vehicle. A hardware-in-the-loop simulation test rig with the actual rack and pinion mechanism of the light commercial vehicle under study was built for this purpose. The steer-by-wire actuator can be placed on either the second pinion, the first pinion or both in the double pinion steering test system used. The hardware and geometry of the steering test rig are identical to the implementation of the steering system in the test vehicle. Unnecessary and expensive road testing is avoided with this approach as most problems are identified and solved in the hardware-in-the-loop simulation phase conducted in the laboratory where the steering subsystem and its controller exist as hardware and the rest of the vehicle exist as a software model running in real time. Hardware-in-the-loop simulation results show the effectiveness of the proposed controller design in tracking desired steering dynamics.

This paper presents the development of a transient active control (TABC) system for the Ford Transit Connect light commercial vehicle using semi active suspensions. The control objective is to improve the ride comfort and road holding together with achieving roll and pitch stability using four semi active suspension dampers, hence called transient active body control. Semi-active control algorithms such as sky-hook, ground-hook and hybrid are applied to each suspension while the roll and pitch stabilizing controllers are designed separately and interfere with the local semi-active controllers through a supervisory control algorithm, if necessary. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed technique.

In a Hybrid Electric Vehicle (HEV), the main aim is to decrease the fuel consumption and emissions without significant loss of driving performance. Maximizing Overall Efficiency Strategy (MOES) algorithm, presented here, distributes the power demand among the available paths to the wheels to improve fuel economy. In MOES, the vehicle is considered as a system whose input and output are power capability of consumed fuel and actual power transferred to the road, respectively. The aim of the strategy is to maximize the overall efficiency of the vehicle determined as the ratio of output power to input power. The control algorithm and driver model were prepared within Simulink and used to drive the Carmaker model of the vehicle which is a Ford Transit hybrid electric research prototype van. Simulations were carried out in 3 modes of the vehicle; conventional mode, regenerative braking only mode and full MOES mode to analyze the role of optimization better.

It has been discussed in numerous prior studies that in-neutral coasting, or sailing, can accomplish considerable amount of fuel saving when properly used. The driving maneuver basically makes the vehicle sail in neutral gear when propulsion is unnecessary. By disengaging a clutch or shifting the gear to neutral, the vehicle may better utilize its kinetic energy by avoiding dragging from the engine side. This strategy has been carried over to series production recently in some of the vehicles on the market and has become one of the eco-mode features available in current vehicles. However, the duration of coasting must be long enough to attain more fuel economy benefit than Deceleration Fuel Cut-Off (DFCO) - which exists in all current vehicle powertrain controllers - can bring. Also, the transients during shifting back to drive gear can result in a drivability concern.

This paper is on the application of electric power assisted steering and yaw stability control to a light commercial vehicle. An active steering system is developed and used for both purposes. Steering system and vehicle dynamics models are derived and built in Simulink and their response is compared to that of a validated Adams/Chassis model of the vehicle. A boost curve type electric power assisted steering controller and a yaw stability control system based on the model regulator steering controller are developed. Their performance is demonstrated through simulation results. A steering test rig built for safely developing steering controllers in a hardware-in-the-loop setting is introduced. Details of the experimental vehicle with active steering, built to test the concepts developed in the paper is also presented.

Connected vehicle (CV) technology is among the most heavily researched areas in both the academia and industry. The vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities enable critical situational awareness. In some cases, these vehicle communication safety capabilities can overcome the shortcomings of other sensor safety capabilities because of external conditions such as 'No Line of Sight' (NLOS) or very harsh weather conditions. Connected vehicles will help cities and states reduce traffic congestion, improve fuel efficiency and improve the safety of the vehicles and pedestrians. On the road, cars will be able to communicate with one another, automatically transmitting data such as speed, position, and direction, and send alerts to each other if a crash seems imminent. The main focus of this paper is the implementation of Cooperative Collision Avoidance (CCA) for connected vehicles.

In this paper, the optimum linear/nonlinear spring and linear/nonlinear damper force versus displacement and force versus velocity characteristic functions, respectively, are determined using simple lumped parameter models of a quarter car front independent suspension and a half car rear solid axle suspension of a light commercial vehicle. The complexity of a nonlinear function optimization problem is reduced by determining the shape a priori based on typical shapes supplied by the car manufacturer and then scaling it up or down in the optimization process. The vehicle ride and handling responses are investigated considering models of increased complexity. The linear and nonlinear optimized spring characteristics are first obtained using lower complexity lumped parameter models. The commercial vehicle dynamics software Carmaker is then used in the optimization as the higher complexity, more realistic model.

Autonomous vehicle (AV) algorithms need to be tested extensively in order to make sure the vehicle and the passengers will be safe while using it after the implementation. Testing these algorithms in real world create another important safety critical point. Real world testing is also subjected to limitations such as logistic limitations to carry or drive the vehicle to a certain location. For this purpose, hardware in the loop (HIL) simulations as well as virtual environments such as CARLA and LG SVL are used widely. This paper discusses a method that combines the real vehicle with the virtual world, called vehicle in virtual environment (VVE). This method projects the vehicle location and heading into a virtual world for desired testing, and transfers back the information from sensors in the virtual world to the vehicle.

Autonomous vehicle technology has been developing rapidly in recent years. Vehicle parametric uncertainty in the vehicle model, variable time delays in the CAN bus based sensor and actuator command interfaces, changes in vehicle sped, sensitivity to external disturbances like side wind and changes in road friction coefficient are factors that affect autonomous driving systems like they have affected ADAS and active safety systems in the past. This paper presents a robust control architecture for automated driving systems for handling the abovementioned problems. A path tracking control system is chosen as the proof-of-concept demonstration application in this paper. A disturbance observer (DOB) is embedded within the steering to path error automated driving loop to handle uncertain parameters such as vehicle mass, vehicle velocities and road friction coefficient and to reject yaw moment disturbances.

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This paper concentrates on low speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of campus as an initial autonomous vehicle (AV) pilot test route for the deployment of low speed autonomous shuttles. This paper presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment.

In recent years, there has been increasing research on automated driving technology. Autonomous vehicle path following performance is one of significant consideration. This paper presents discrete time design of robust PD controlled system with disturbance observer (DOB) and communication disturbance observer (CDOB) compensation to enhance autonomous vehicle path following performance. Although always implemented on digital devices, DOB and CDOB structure are usually designed in continuous time in the literature and also in our previous work. However, it requires high sampling rate for continuous-time design block diagram to automatically convert to corresponding discrete-time controller using rapid controller prototyping systems. In this paper, direct discrete time design is carried out. Digital PD feedback controller is designed based on the nominal plant using the proposed parameter space approach.

Many smart cities and car manufacturers have been investing in Vehicle to Infrastructure (V2I) applications by integrating the Dedicated Short-Range Communication (DSRC) technology to improve the fuel economy, safety, and ride comfort for the end users. For example, Columbus, OH, USA is placing DSRC Road Side Units (RSU) to the traffic lights which will publish traffic light Signal Phase and Timing (SPaT) information. With DSRC On Board Unit (OBU) equipped vehicles, people will start benefiting from this technology. In this paper, to accelerate the V2I application development for Connected and Autonomous Vehicles (CAV), a Hardware in the Loop (HIL) simulator with DSRC RSU and OBU is presented. The developed HIL simulator environment is employed to implement, develop and evaluate V2I connected vehicle applications in a fast, safe and cost-effective manner.

Deployment of autonomous vehicles requires an extensive evaluation of developed control, perception, and localization algorithms. Therefore, increasing the implemented SAE level of autonomy in road vehicles requires extensive simulations and verifications in a realistic simulation environment before proving ground and public road testing. The level of detail in the simulation environment helps ensure the safety of a real-world implementation and reduces algorithm development cost by allowing developers to complete most of the validation in the simulation environment. Considering sensors like camera, LiDAR, radar, and V2X used in autonomous vehicles, it is essential to create a simulation environment that can provide these sensor simulations as realistically as possible.

Connected Vehicle (CV) technologies have been progressing rapidly in the US. The capability afforded by Vehicle-to-Vehicle (V2V) communication improves situational awareness and provides advantages for many of the traffic problems caused by reduced visibility or No-Line-of-Sight situations, being useful for both autonomous and non-autonomous driving. Additionally, with the traffic light Signal Phase and Timing (SPaT) and Map Data (MAP) information and other advisory information provided with Vehicle-to-Infrastructure (V2I) communication, outcomes which benefit the driver in the long run, such as reducing fuel consumption with speed regulation or decreasing traffic congestion through optimal speed advisories, providing red light violation warning messages and intersection motion assist messages for collision-free intersection maneuvering are all made possible.

This study is focused on exploring the possibilities of using camera and route planner images for autonomous driving in an end-to-mid learning fashion. The overall idea is to clone the humans’ driving behavior, in particular, their use of vision for ‘driving’ and map for ‘navigating’. The notion is that we humans use our vision to ‘drive’ and sometimes, we also use a map such as Google/Apple maps to find direction in order to ‘navigate’. We replicated this notion by using end-to-mid imitation learning. In particular, we imitated human driving behavior by using camera and route planner images for predicting the desired waypoints and by using a dedicated control to follow those predicted waypoints. Besides, this work also places emphasis on using minimal and cheaper sensors such as camera and basic map for autonomous driving rather than expensive sensors such Lidar or HD Maps as we humans do not use such sophisticated sensors for driving.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.