Trajectory planning is a major challenge in robotics and autonomous vehicles, ensuring both efficient and safe navigation. The primary objective of this work is to generate an optimal trajectory connecting a starting point to a destination while meeting specific requirements, such as minimizing travel distance and adhering to the vehicle’s kinematic and dynamic constraints. The developed algorithms for trajectory design, defined as a sequence of arcs and straight segments, offer a significant advantage due to their low computational complexity, making them well-suited for real-time applications in autonomous navigation. The proposed trajectory model serves as a benchmark for comparing actual vehicle paths in trajectory control studies. Simulation results demonstrate the robustness of the proposed method across various scenarios.

Soundouss, Halima, Msaaf, Mohammed, Belmajdoub, Fouad

Letter from the Guest Editors

12-08-02-0011

To be published on 06/06/2025

Letter from the Guest Editors

Liang, Ci, Törngren, Martin

A Microgrid Vehicle-to-Grid Energy Scheduling Optimization Algorithm Based on Non-Dominated Sorting Genetic Algorithm II

2025-01-5035

05/30/2025

With the rapid expansion of the electric vehicle (EV) market, the frequency of grid-connected charging has concentrated primarily during peak hours, notably from 7:00 a.m. to 10:00 a.m. and 6:00 p.m. to 10:00 p.m., resulting in substantial demand surges during both morning and evening periods. Such uncoordinated charging patterns pose potential challenges to the stability and economic efficiency of power systems. As vehicle-to-grid (V2G) technology advances, facilitating bidirectional energy exchange between EVs and smart grids, the need for optimized control of EV charging and discharging behaviors has become critical to achieving effective peak shaving and valley filling in the grid. This paper proposes a microgrid energy scheduling optimization algorithm based on existing smart grid and EV charging control technologies. The method establishes a multi-objective optimization model with EVs’ 24-h charging and discharging power as decision variables and microgrid load rate, load

Fan, Longyu, Chen, Yuxin, Zhang, Dacai

Improving the Working Performance of Sliding Friction Pairs Using Optimal Asymmetric Textures

04-18-03-0016

05/29/2025

Optimizing the parameters of asymmetric textures (AT) designed on the surface of sliding frictional pairs (SFP) can make each texture more reasonably distributed. Thereby, the oil film thickness can be more stable; and the lubrication and load ability of SFP can be improved. To clarify this issue, based on the SFP’s lubricating model added by AT using the rectangular structure, parameters of AT including the angle between the horizontal axe and bottom surface (φij), the angle between the lateral axe and bottom surface (γij), and texture’s depth (hij) are optimized. The study results show that the parameters of φij, γij, and hij of AT optimized can create the p (hydrodynamic pressure of liquid) better than the symmetric textures. Significantly, the pmax and load ability of the liquid in the SFP using optimal AT have been greatly increased compared to the liquid in the SFP using the symmetric textures. Accordingly, the results are an important reference for the design and distribution of

Wang, Cuifang, Zhang, Lu

Design Space Exploration of a Continuous Rubber Track System via Surrogate Modeling

02-18-04-0023

05/26/2025

Continuous rubber track systems for heavy applications are typically designed using multiple iterations of full-scale physical prototypes. This costly and time-consuming approach limits the possibility of exploring the design space and understanding how the design space of that kind of system is governed. A multibody dynamic simulation has recently been developed, but its complexity (due to the number of model’s inputs) makes it difficult to understand and too expensive to be used with multi-objective optimization algorithms (approximately 3 h on a desktop computer). This article aims to propose a first design space exploration of continuous rubber track systems via multi-objective optimization methods. Using an existing expensive multibody dynamic model as original function, surrogate models (artificial neural networks) have been trained to predict the simulation responses. These artificial neural networks are then used to explore the design space efficiently by using optimization

Faivre, Antoine, Rancourt, David, Plante, Jean-Sébastien

Development of an Intelligent Instrumented Tire for Real-Time Hydroplaning Risk Estimation

10-09-03-0027

05/21/2025

This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a

Vilsan, Alexandru, Sandu, Corina, Anghelache, Gabriel, Warfford, Jeffrey

Leveraging Advanced Data Technologies for Smart Traffic Management

EPR2025008

05/20/2025

The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Ercisli, Safak

Evaluation of Occupant Injury Risk in a Transport Aircraft Crash Test Environment

02-18-02-0011

05/16/2025

Researchers at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) previously conducted a full-scale crash test of a Fokker F28 MK1000 aircraft to study occupant injury risks. The goal of the current study was to investigate the injury predictions of the Global Human Body Models Consortium (GHBMC) and Total Human Model for Safety (THUMS) occupant models in the tested aircraft crash condition and explore possible utilization of both human body models (HBMs) in this context. Eight crash conditions were simulated utilizing each of the models. The HBMs were positioned in two postures, a neutral upright posture with hands resting on the legs and feet contacting the floor and a braced posture with head and hand contact with the forward seat back. Head and neck injury metrics and lumbar vertebra axial force were calculated and compared for all simulations. Both HBMs reported similar kinematic responses in the simulated impact conditions. However, the GHBMC

Jones, Nathaniel, Putnam, Jacob, Untaroiu, Costin Daniel

V2P-Based Speed Guidance Method for Avoiding Vehicle–Pedestrian Collisions at Intersections

12-09-01-0006

05/14/2025

Conflicts between vehicles and pedestrians at unsignalized intersections occur frequently and often result in serious consequences. In order to alleviate traffic flow congestion at unsignalized intersections caused by accidents, reduce vehicle congestion time and waiting time, and improve intersection safety as well as intersection access efficiency, a speed guidance algorithm based on pedestrian-to-vehicle (P2V) and vehicle-to-pedestrian (V2P) communication technologies is proposed. The method considers the heading angle (direction of motion) of vehicles and pedestrians and combines the post encroachment time (PET) and time to collision (TTC) to determine whether there is a risk of collision, so as to guide the speed of vehicles. Network simulator NS3 and traffic flow simulation software SUMO are used to verify the effectiveness of the speed guidance strategy proposed in this article. The experimental findings demonstrate that the speed guidance strategy introduced in this article

Sun, Yuanyuan, Wang, Kan, Liu, Weizhen, Li, Wenli

Proposed Reformulation of Brain Injury Criteria (BrIC) Using Head Rotation-Induced Brain Injury Thresholds Simulated and Derived Directly from A Subhuman Primate Finite Element Model

2025-22-0003

05/13/2025

Recent studies have found that Brain Injury Criteria (BrIC) grossly overpredicts instances of real-world, severe traumatic brain injury (TBI). However, as it stands, BrIC is the leading candidate for a rotational head kinematics-based brain injury criteria for use in automotive regulation and general safety standards. This study attempts to understand why BrIC overpredicts the likelihood of brain injury by presenting a comprehensive analysis of live primate head impact experiments conducted by Stalnaker et al. (1977) and the University of Pennsylvania before applying these injurious conditions to a finite element (FE) monkey model. Data collection included a thorough analysis and digitization of the head impact dynamics and resulting pathology reports from Stalnaker et al. (1977) as well as a representative reconstruction of the Penn II baboon diffuse axonal injury (DAI) model. Computational modeling techniques were employed on a FE Rhesus monkey model, first introduced by Arora et al

Demma, Dominic R., Tao, Ying, Zhang, Liying, Prasad, Priya

Development of a Generic Nearside Impact Test Fixture for Evaluating In-Vehicle Crashworthiness of Wheelchairs

2025-22-0002

05/13/2025

Current voluntary standards for wheelchair crashworthiness only test under frontal and rear impact conditions. To help provide an equitable level of safety for occupants seated in wheelchairs under side impact, we developed a sled test procedure simulating nearside impact loading using a fixed staggered loading wall. Publicly available side impact crash data from vehicles that could be modified for wheelchair use were analyzed to specify a relevant crash pulse. Finite element modeling was used to approximate the side impact loading of a wheelchair during an FMVSS No. 214 due to vehicle intrusion. Validation sled tests were conducted using commercial manual and power wheelchairs and a surrogate wheelchair base fixture. Test procedures include methods to position the wheelchair to provide consistent loading for wheelchairs of different dimensions. The fixture and procedures can be used to evaluate the integrity of wheelchairs under side impact loading conditions.

Boyle, Kyle, Hu, Jingwen, Manary, Miriam, Orton, Nichole R., Klinich, Kathleen D.

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