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This paper presents a nonlinear control approach to achieve good performances in vehicle path following and collision avoidance when the vehicle is driving under cruise highway conditions. Nonlinear model predictive control (NLMPC) is adopted to achieve online trajectory control based on a simplified vehicle model. GMRES/Continuation algorithm is used to solve the online optimization problem. Simulations show that the proposed controller is capable of tracking the desired path as well as avoiding the obstacles.

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.

Many studies have been carried out to optimize the aerodynamic performances of a single car or a single vehicle. In present days the traffic increases and sophisticated technologies are developing to guarantee the drivers safety, to minimize the fuel consumption and be more environmentally friendly. Within this research area a new technique that is being studied is Platooning: this means that different vehicles travel in a configuration that minimizes the aerodynamic drag and therefore the fuel consumption and the longitudinal space. In the present study platoons with different vehicles and configurations are taken into account, to analyze the influence of car shape and relative distance between the vehicles. The research has been carried out using CFD techniques to investigate the different flow fields around different platoons, while wind tunnel tests have been used to validate the results of the CFD simulations.

Energy management plays a key role in achieving higher fuel economy for plug-in hybrid electric vehicle (PHEV) technology; the state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining the fuel consumed. The energy management algorithm should be designed to meet all driving scenarios while achieving the best possible fuel economy. The knowledge of the power requirement during a driving trip is necessary to achieve the best fuel economy results; performance of the energy management algorithm is closely related to the amount of information available in the form of road grade, velocity profiles, trip distance, weather characteristics and other exogenous factors. Intelligent transportation systems (ITS) allow vehicles to communicate with one another and the infrastructure to collect data about surrounding, and forecast the expected events, e.g., traffic condition, turns, road grade, and weather forecast.

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.

Highway and roadway surface measurement is a practice that has been ongoing for decades now. This sort of measurement is intended to ensure a safe level of road perturbances. The measurement may be conducted by a slow moving apparatus directly measuring the elevation of the road, at varying distance intervals, to obtain a road profile, with varying degrees of resolution. An alternate means is to measure the surface roughness at highway speeds using accelerometers coupled with high speed distance measurements, such as laser sensors. Vehicles out rigged with such a system are termed inertial profilers. This type of inertial measurement provides a sort of filtered roadway profile. Much research has been conducted on the analysis of highway roughness, and the associated metrics involved. In many instances, it is desirable to maintain an off-road course such that the course will provide sufficient challenges to a vehicle during durability testing.

This paper presents some of the technical considerations that underlie the development of the master plan and the establishment of design specifications for Ohio's Highway Research Laboratory. It describes the overall features of the master plan and discusses some of the critical design features as these relate to the various tracks and other field facilities. The development of the master plan was guided by a study of the layout of existing proving grounds and by the experience gained over the years in their operations. It was guided furthermore by a set of principles relating to operational considerations, considerations of flexibility in the layout, land utilization, safety, capacity, and cost. Finally, it was guided by an indication of future research and development needs as expressed by researchers and potential sponsors in both government and industry.

The sensitivity factor, λ, of stimulus-response car following equations was computed, based on response times, τ, obtained from aerial survey data. Vehicles of a platoon are investigated as they approach, proceed through, and leave behind a kinematic disturbance, and an inherent local and asymptotic instability is discovered. Aerial survey data is used in a numerical example to demonstrate how multiple rear-end collisions can be triggered by one vehicle. A driver aid system, informing drivers about the differential velocity between lead and following vehicles, could improve stability, although the final answer appears to lie in automated or semi-automated longitudinal control systems.

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.

This paper proposes the use of an on-demand, ride hailed and ride-Shared Autonomous Vehicle (SAV) service as a feasible solution to serve the mobility needs of a small city where fixed route, circulator type public transportation may be too expensive to operate. The presented work builds upon our earlier work that modeled the city of Marysville, Ohio as an example of such a city, with realistic traffic behavior, and trip requests. A simple SAV dispatcher is implemented to model the behavior of the proposed on-demand mobility service. The goal of the service is to optimally distribute SAVs along the network to allocate passengers and shared rides. The pickup and drop-off locations are strategically placed along the network to provide mobility from affordable housing, which are also transit deserts, to locations corresponding to jobs and other opportunities.

For realistic traffic modeling, real-world traffic calibration data is needed. These data include a representative road network, road users count by type, traffic lights information, infrastructure, etc. In most cases, this data is not readily available due to cost, time, and confidentiality constraints. Some open-source data are accessible and provide this information for specific geographical locations, however, it is often insufficient for realistic calibration. Moreover, the publicly available data may have errors, for example, the Open Street Maps (OSM) does not always correlate with physical roads. The scarcity, incompleteness, and inaccuracies of the data pose challenges to the realistic calibration of traffic models. Hence, in this study, we propose an approach based on spatial interpolation for addressing sparsity in vehicle count data that can augment existing data to make traffic model calibrations more accurate.

With high maneuverability and heavy-duty load capacity, articulated steer vehicles (ASV) are widely used in construction, forestry and mining sectors. However, the steering process of ASV is much different from wheeled steer vehicles and tractor-trailer vehicles. Unsuitable steering control in path following could easily give rise to the “snaking” behaviour, which greatly reduces the safety and stability of ASV. In order to achieve precise control for ASV, a novel path tracking control method is proposed by virtual terrain field (VTF) method. A virtual U-shaped terrain field is assumed to exist along the reference path. The virtual terrain altitude depends on the lateral error, heading error, preview distance and road curvature. If the vehicle deviates from the reference line, it will be pulled back to the lowest position under the influence of additional lateral tire forces which are caused by the virtual banked road.

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.

Aim of this study is to analyze the benefits of the measures provided by smart tyres on tyre-road friction coefficient and vehicle sideslip angle estimation. In particular, a smart tyre constituted by 2 tri-axial accelerometers glued on the tyre inner liner is considered which is able to provide the measures of the tyre-road contact forces once per wheel turn. These measures are added to the ones usually present onboard vehicle (steer angle, lateral acceleration and yaw rate) and following included into an Extended Kalman Filter (EKF) based on a single-track vehicle model. Performance of the proposed observer is evaluated on a series of handling maneuvers and its robustness to road bank angle, tyre and vehicle parameters variation is discussed.

The Linear Time Varying (LTV) Model Predictive Control (MPC) is a linear model predictive control based on linearization of the nonlinear vehicle model. The linearization is carried out consideing each vehicle state. The developed model is able to steer to avoid obstacles and follow a given path. Once the optimal parameters are found, both in terms of trajectory following and real-time performances, the LTV-MPC is used for assessing the limit vehicle conditions as a function of the vehicle forward target speed, the obstacle shape as well as the road conditions (both dry and wet road conditions were taken into account). It is shown that, to avoid collisions, given performances of the vehicle brakes and of the mounted sensors are required.

For passenger cars, individual tyre model parameters, used in vehicle models able to simulate vehicle handling behavior, are traditionally derived from expensive component indoor laboratory tests as a result of an identification procedure minimizing the error with respect to force and slip measurements. Indoor experiments on agricultural tyres are instead more challenging and thus generally not performed due to tyre size and applied forces. However, the knowledge of their handling characteristics is becoming more and more important since in the next few years, all agricultural vehicles are expected to run on ordinary asphalt roads at a speed of 80km/h. The present paper presents a methodology to identify agricultural tyres' handling characteristics based only on the measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed and steer angle) during standard handling maneuvers (step-steers, J-turns, etc.), instead than during indoor tests.

At first glance RDE seems to be a road testing topic only, mistakenly. While Type approval test must be performed at the Road and Chassis Dyno, development work beforehand delivers solutions fulfilling the demanded legislation limits. Making the right development steps and decisions will lead to a technical solution within economy of scales. Much of this work done happens on engine testbeds and Real Driving Emissions (RDE) per UN-ECE legislation or the new test cycle for the chassis dyno according WLTP (Worldwide Harmonized Light-Duty Test Procedure) will not change that. The question is, are engine test beds fit for this new challenge or are changes required? One characteristic element of RDE is the randomness of operating conditions generated by a road drive. There must be found a way, to achieve RDE relevant test conditions in a most reproducible manner.

This paper and the associated lecture present an overview of technology trends and of market and business opportunities created by technology, as well as of the challenges posed by environmental and economic considerations. Commercial vehicles are one of the engines of our economy. Moving goods and people efficiently and economically is a key to continued industrial development and to strong employment. Trucks are responsible for nearly 70% of the movement of goods in the USA (by value) and represent approximately 300 billion of the 3.21 trillion annual vehicle miles travelled by all vehicles in the USA while public transit enables mobility and access to jobs for millions of people, with over 10 billion trips annually in the USA creating and sustaining employment opportunities.

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.