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The article addresses both urban and highway driving situations and some of the associated optimization problems. After the introduction of a suitable powertrain model, several optimization problems of practical importance are formulated. ...In particular, the case of energy-optimal speed trajectories is highlighted, and it is shown that for urban driving, energy savings can exceed 50%, assuming complete traffic situational awareness. It is further demonstrated that “urban platooning” is an attractive and viable implementation option for energy-optimal speed trajectories. ...It is further demonstrated that “urban platooning” is an attractive and viable implementation option for energy-optimal speed trajectories.

Abstract In the last decades we have witnessed an increasing number of military operations in urban environments. Complex urban operations require high standards of training, equipment, and personnel. ...This article describes the method for developing a concept for an urban operations vehicle by characterization of the urban environment, deduction of key issues, evaluation of related prototyping, science fiction story-typing of the requirements for such a vehicle, and comparison with field-proven and scalable solutions.

Under the background of intelligent transportation, it is particularly important to effectively evaluate the rail transit as the framework of urban public transport in this study, and fuzzy mechanism is introduced to optimize the support vector machine (SVM), and on this basis, analytic hierarchy process (AHP) and SVM are combined to improve the classification accuracy and improve the rail transit operation safety evaluation index system.

The proposed controller is validated with the help of an integrated simulation environment comprising of MATLAB, VISSIM, and the Network Simulator (NS3), the controller performance is analyzed for both urban arterial and highway traffic scenarios. The simulator capabilities are demonstrated by testing platooning under different traffic conditions.

Abstract Recently, high-resolution sensors capable of multiple detections (MDs) per object are available for perception applications in autonomous or semi-autonomous vehicles. Conventional multi-target tracking (MTT) approaches start with the point-target assumption and thus cannot be applied directly to the MDs of high-resolution sensors. A popular solution widely used in literature starts with a measurement partitioning approach, followed by repurposing conventional tracking algorithms to accommodate the resulting partitions. However, the computational requirement increases combinatorially, especially under multi-sensor applications that also independently return multiple radar reflections as in the automotive radar sensors used in this work. Thus, a hybrid approach that combines a clustering technique (such as DBSCAN) to alleviate the computational complexity and an MD tracking scheme that admits multiplicity of the target detections is employed.

Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. ...The method is evaluated for naturalistic road traffic data at urban intersections from the inD and the Silicon Valley Intersections datasets. For this purpose, it is analyzed with which clustering approach (K-means, hierarchical clustering, and DBSCAN) the scenario extraction method performs best (referring to an elaborate rule-based implementation).

Abstract It is a challenge to find a safe trajectory for automated vehicles in urban environments with pedestrians. The prediction of future movements with 100% certainty is impossible, if the intention is unknown. ...This approach is useful for cooperative vehicle systems, with historical movement data in a fixed urban environment (e.g., intersection) and the premise that pedestrians follow typical movement data.

Abstract Black spot distribution is a major indicator of urban traffic safety. Identifying the spatial distribution of black spots at urban centers is fundamental to understanding the causes, types, and severity of accidents.

Abstract With the sustainable development of the social economy and the continuous maturity of science and technology, urban rail transit has developed rapidly. It solved the problems of urban road load and people’s travel and brought about the problem of rail transit passenger congestion.

Abstract Urban road intersections are the most common complex traffic conditions, especially in intersections without signal lights, when a right-of-way conflict occurs between vehicles, the future trajectory of vehicles is full of uncertainty due to the driver’s personalized driving style and the difference in recognition of relevant driving rules.

While fully automated privately owned AVs are not yet commercially available to the public, AVs such as robotaxis, shuttles, and delivery bots are becoming increasingly common, especially in densely populated urban areas. At the same time, new types of micromobility devices, such as e-scooters, are being introduced into the transportation infrastructure, often in the same areas.

A mild, an average, and a sporty driver behavior is examined on a highway, a rural, and an urban driving cycle. The driving cycles are used as input to calculate the thermal effects in an initial model.

For various applications of AGVs, such as urban and off-road autonomous driving, the features of driving conditions and vehicle kinodynamics are outlined, and sample-tailored motion-planning algorithms built upon relevant canonical techniques are briefly introduced.

Finally, both strategies have been assessed via numerical simulations on two different driving cycles: the Worldwide harmonized Light vehicles Test Cycle (WLTC) and the Transport for London cycle (TfL), an urban driving cycle that is selected as a worst-case scenario for the thermal management of the aftertreatment system. ...On the urban cycle, only the second strategy is able to ensure the catalyst heating in a reasonable timespan.

The methods were used to select architecture for Class 8 urban, regional, and line haul applications. When compared to traditional load-following power-split controllers, an energy management power-split controller can increase system energy efficiency by up to 19.5%. ...To achieve TCO parity with the 600 A non-isolated DC-DC converter case, the specific choice of the fuel cell DC-DC converter to achieve a target power output based on current levels (from 500 A to 2400 A) shows that efficiency decreases and cost increases due to the higher current, requiring fuel cell prices to decrease by $50–$100/kW, $60–$110/kW, and $100–$220/kW for urban, regional, and line haul applications, respectively. Key recommendations for powertrain system architectures are provided, with specifics based on vehicle dynamics, mission and application characteristics, end customer use-case profile, critical powertrain component costs, and architecture selection cost function.

Abstract For safe and comfortable automated driving in the urban domain, especially in complex geometries as intersections, the prediction of surrounding traffic participants is fundamental.

In the case of the scaled urban dynamometer driving schedule, the energy loss and the maximum spacing between the autonomous vehicle and the human-driven vehicle decreases by 6% and 18%, respectively, when compared with the baseline model predictive control without the consideration of interaction between the autonomous vehicle and the human-driven vehicle.

To evaluate the performance of the proposed energy management strategy (EMS), the average efficiency of the engine and the motor was analyzed in an urban area, high-speed area, and the whole area. The comprehensive fuel consumption was used as the energy consumption index, and the battery capacity loss under the transient conditions was amplified to 10 years as the evaluation battery life index.

To address all these concerns, in this study we develop a co-simulation platform integrating Unity, Simulation of Urban MObility (SUMO), and Amazon Web Services (AWS), where Unity provides realistic driving experience and simulates on-board sensors; SUMO models realistic traffic dynamics; and AWS provides serverless cloud computing power and personalized data storage.

To compare the performance of the conventional and the fuel cell electric powertrains, both are numerically modeled and analyzed in AVL software for the Urban Driving Cycle (UDC) at various cargo loads. The final drive transmission (FDT) speed and input and output torques are first matched for both powertrains.