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Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model.

For Automated Vehicles (AVs) to be successful, they must integrate into society in a way that makes everyone confident in how AVs work to serve people and their communities. This integration requires that AVs communicate effectively, not only with other vehicles, but with all road users, including pedestrians and cyclists. One proposed method of AV communication is through an external human-machine interface (eHMI). While many studies have evaluated eHMI solutions, few have considered their compliance with relevant Federal Motor Vehicle Safety Standards (FMVSS) and their scalability. This study evaluated the effectiveness of a lightbar eHMI to communicate AV intent by measuring user comprehension of the eHMI and its impact on pedestrians’ trust and acceptance of AVs.

The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent.

By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

The interaction of Connect and Automated vehicles (CAV) with regular vehicles in the traffic stream has been extensively researched. Most studies, however, focus on calibrating driver behavior models for CAVs based on various levels of automation and driver aggressiveness. Other related studies largely focus on the coordination of CAVs and infrastructure like traffic signals to optimize traffic. However, the effects of different strategic flow management of CAVs in the traffic stream in the comparative scenario-based analysis is understudied. Thus, this study develops a framework and simulations for integrating CAVs in a corridor section. We developed a calibrated model with CAVs for a corridor section in Corktown, Michigan, and simulate how dedicated CAV lane operations can be implemented without significant change in existing infrastructure.

Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.

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.

The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs.

People and their communities are looking for transportation solutions that reduce travel time, improve well-being and accessibility, and reduce emissions and traffic congestion. Although new mobility services like ride-hailing advertise improvements in these areas, closer inspection has revealed a discrepancy between industry claims and reality. Key decision-makers, including citizens, cities and enterprise, and mobility service providers have the opportunity to leverage connected vehicle and connected device data through cloud-based APIs. We propose a GHG data analytics framework that functions on top of a cloud platform to provide unique system-level perspectives on operating transportation services, from procuring the most environmentally and people friendly vehicles to scheduling and designing the services based on data insights.

This paper summarizes the development of a wireless message from infrastructure-to-vehicle (I2V) for safety applications based on Dedicated Short-Range Communications (DSRC) under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). During the development of the Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure (RSZW/LC) safety applications [1], the Basic Information Message (BIM) was developed to wirelessly transmit infrastructure-centric information. The Traveler Information Message (TIM) structure, as described in the SAE J2735, provides a mechanism for the infrastructure to issue and display in-vehicle signage of various types of advisory and road sign information. This approach, though effective in communicating traffic advisories, is limited by the type of information that can be broadcast from infrastructures.

This paper summarizes the validation of prototype vehicle-to-infrastructure (V2I) safety applications based on Dedicated Short Range Communications (DSRC) in the United States under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). After consideration of a number of V2I safety applications, Red Light Violation Warning (RLVW), Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure Warning (RSZW/LC) were developed, validated and demonstrated using seven different vehicles (six passenger vehicles and one Class 8 truck) leveraging DSRC-based messages from a Road Side Unit (RSU). The developed V2I safety applications were validated for more than 20 distinct scenarios and over 100 test runs using both light- and heavy-duty vehicles over a period of seven months. Subsequently, additional on-road testing of CSW on public roads and RSZW/LC in live work zones were conducted in Southeast Michigan.

Automated driving is currently one of the most active areas of research worldwide. While the general progress in developing specific algorithms for perception, planning and control tasks is very advanced, testing and validation of the resulting functions is still challenging due to the large number of possible scenarios and generation of ground-truth. Currently, real world testing and simulations are used in combination to overcome some of these challenges. While real world testing does not suffer from imperfect sensor models and environments, it is expensive, slow and not accurately repeatable and therefore unable to capture all possible scenarios. However, simulation models are not sophisticated enough to fully replace real world testing. In this paper, we propose a workflow that is capable of augmenting real sensor-level data with simulated sensor data.

Gasoline particulate filters (GPF) are used as an efficient solution to reduce particulate matter (PM) emissions on gasoline vehicles. GPFs are ceramic wall-flow filters and are normally located downstream of conventional three-way catalysts (TWC) [1]. The study in this paper is intended to evaluate the impact of drive cycle and ambient temperature on modelled GPF soot accumulation and regeneration. The test data were obtained through real road testing in Chinese cities including Nanjing, Hainan and Harbin. Five 2.0 L gasoline turbo direct-injection (GTDI) prototype vehicles from several China Stage 6 applications were employed for the road tests. The results of the testing indicated that a drive cycle with low engine speed and engine load, like a typical city road in rush hour traffic in Nanjing, had a low probability of generating high GPF temperatures (> 600 °C) and sufficient oxygen to regenerate the GPF.

The unpleasant noise (creak) originated from latch-striker interaction, perceived mainly when the vehicle is submitted to uneven road conditions is generated by stick-slip phenomenon mainly due materials incompatibility of contact surfaces. Generally, eliminate this incompatibility is unfeasible due technical and/or economics constrains; this scenario makes it necessary to act in other fronts to neutralize the effects of that incompatibility. Reduce the coefficient of friction from one of contact surfaces is an alternative that can be easily applied at striker through a thin thickness coating with that property.

The world urbanization is growing rapidly, bringing many challenges for people to move in dense metropolitan regions. Public transportation is not able to attend the whole demand, and individual transportation modes are struggling with traffic congestion and stringent regulations to reduce its attractiveness, such as the license plate restriction in São Paulo. On the other hand, enablers like smartphones mass penetration, GPS connected services and shared economy have opened space to a whole new range of possible solutions to improve people perception on urban mobility. This work aims to evaluate the modal choice behavior models and understand the success factor of current mobility solutions in the city of São Paulo. The data available through origin/destination researches will be used to validate the models used in this work.

Vehicle to Vehicle Communication use case performance heavily relies on market penetration rate. The more vehicles support a use case, the better the customer experience. Enabling these use cases with acceptable quality on vehicles without built-in navigation systems, elaborate map matching and highly accurate sensors is challenging. This paper introduces a simulation framework to assess system performance in dependency of vehicle positioning accuracy for matching approach path traces in Decentralized Environmental Notification Messages (DENMs) in absence of navigation systems supporting map matching. DENMs are used for distributing information about hazards on the road network. A vehicle without navigation system and street map can only match its position trajectory with the trajectory carried in the DENM.

In this work, we outline a process for traffic light detection in the context of autonomous vehicles and driver assistance technology features. For our approach, we leverage the automatic annotations from virtually generated data of road scenes. Using the automatically generated bounding boxes around the illuminated traffic lights themselves, we trained an 8-layer deep neural network, without pre-training, for classification of traffic light signals (green, amber, red). After training on virtual data, we tested the network on real world data collected from a forward facing camera on a vehicle. Our new region proposal technique uses color space conversion and contour extraction to identify candidate regions to feed to the deep neural network classifier. Depending on time of day, we convert our RGB images in order to more accurately extract the appropriate regions of interest and filter them based on color, shape and size. These candidate regions are fed to a deep neural network.

It is estimated that up to 30% of traffic in cities is due to drivers searching for parking. Research suggests that drivers spend an average of 6-14 minutes looking for an available space in London. This increases individual stress levels as well as congestion and pollution. Parking Guidance Systems provide an effective way to reduce parking search time by presenting drivers with dynamic information on parking. An accurate prediction and recommendation analytics algorithm is the key part of the system combining real time cloud-based analytics and historical data trends that can be integrated into a smart parking user application. This paper develops a prediction algorithm based on transient queuing theory and Laplace transform to predict parking occupancy thus predicting open parking locations.

Today, automated vehicles mostly rely on ego vehicle sensors such as cameras, radar or LiDAR sensors that are limited in their sensing capability and range. Vehicle-to-everything (V2X) communication has the potential to appropriately complement these sensors and even allow for a cooperative, proactive interaction of vehicles. As such, V2X communication might play a vital role on the way to smart and efficient traffic solutions. In the public funded research project UK Autodrive, we are currently investigating and experimentally evaluating V2X-based applications based on dedicated short range communication (DSRC). Moreover, the novel application intersection priority management (IPM) is part of the research project. IPM aims at automating intersections in such a way that vehicles can pass safely and even more efficiently without the use of traffic lights or signs.