Browse Topic: Transportation Systems

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Planning Charging Infrastructure for Electric Freight Corridors2026-01-0469To be published on 04/07/2026
With the rapid development of electric vehicles, electric freight transportation is gradually increasing. However, the construction of charging infrastructure on expressways has lagged far behind, becoming a key bottleneck in the development of new energy freight transportation. This paper proposes an optimization system for the layout of charging stations in the rest areas of the Beijing–Hong Kong–Macao Expressway, based on freight logistics information. The system combines the Flow Capturing Location-Allocation Model (FCLM) with an improved genetic algorithm to dynamically analyze traffic flow, service area distribution, and charging demand, thereby achieving an optimized layout of charging stations. By simulating traffic flow variations under different scenarios, the study analyzes the spatiotemporal distribution of charging demand and optimizes the selection of charging station sites and charger allocation. Experimental results demonstrate that the proposed optimization method
Guo, Haifeng, Zhang, Jingzhong, Lian, Jintao
A Real-Time Vehicle Detection and Counting System Using a GUI Interface.2026-01-0156To be published on 04/07/2026
The growing need for intelligent traffic monitoring and environmental control has fuelled the importance of automated vehicle detection and counting systems. Fuel consumption estimation, vehicular emissions calculations, and traffic pattern comprehension- all play a vital role in shaping sustainable environmental policies. Therefore, accurate identification and counting of vehicles is essential. In this paper, we have shown the development of a real-time vehicle detection and counting application that uses computer vision and deep learning techniques to effectively detect, categorise, and count several types of vehicles on Indian roads. The computer vision vehicle counting application uses the YOLO (You Only Look Once) deep learning model for real-time identification and categorisation of diverse vehicle types, including cars, buses, trucks, and motorbikes [1]. A tracking module based on the SORT (Simple Online and Real-time Tracking) algorithm ensures unique identification of vehicles
Gusain, Ashna, Maurya, Akshay, PATHAK, SUNIL, Khan, Tuhin
Integrated Longitudinal and Lateral Control for Autonomous Truck Platooning in Curved Scenarios with 5G-V2X Uncertainties2026-01-0168To be published on 04/07/2026
This paper presents a practical feedback control strategy for high-fidelity autonomous truck platooning, addressing the critical challenge of negotiating curved roads under 5G-V2X communication uncertainties. A nonlinear dynamic model is developed, integrating both longitudinal and lateral vehicle dynamics to simulate a five-truck platoon. The proposed hierarchical control architecture consists of two key components: (1) an Adaptive Cruise Control (ACC) system for maintaining safe inter-vehicle distances using a PD controller, and (2) a path-following lateral controller that ensures accurate lane-keeping on curved sections by tracking a predefined road centerline profile. The 5G-V2X sensor uncertainty, a major source of measurement noise in vehicle-to-everytyhing communication, is modeled as additive Gaussian noise. Extensive simulations under a scenario involving a transition from a straight road to a constant-radius curve (R=500m) demonstrate the efficacy of the proposed method
zhou, junyu, Zeng, Dequan, Petitti, Antonio, xiong, min, GAO, LETIAN, Jiang, Xiaomeng, lu, jun, Yu, Yinquan, CARBONE, GIUSEPPE, Hu, Yiming, Nawaz, Aamir, Linh, Nguyen Vu
Safety-Constrained Control of Nonlinear Mobility Systems Using Reference Governor2026-01-0033To be published on 04/07/2026
Ensuring safe operation and reliable control of mobility systems remains a significant challenge, particularly for nonlinear and high-dimensional applications subject to external disturbances with hard constraints and limited computational resources in real-time implementations. A reference governor (RG) can enforce constraints using an add-on scheme that preserves the pre-stabilizing controller while balancing the need to satisfy other requirements, including reference tracking and disturbance rejection. Thus, in this paper, we exploit RG-based strategies focusing on nonlinear mobility systems. While the method is generalizable to other applications, such as waypoint following for autonomous driving, the flight dynamics of a quadrotor system with twelve states are used as an example. We implement a disturbance rejection RG to satisfy safety constraints and track set points. To handle nonlinearity, we propose an optimal strategy to quantify the maximum deviation between the nonlinear
Dong, Yilong, Li, Huayi
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-0041To be published on 04/07/2026
This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging Reinforcement Learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating
Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik
Thermo-Mechanical Analysis of 1U CubeSat Structures using ANSYS: Insights for Mobility Applications2026-01-0145To be published on 04/07/2026
Particularly for small satellites like CubeSats, the growing commercialization and downsizing of space technology have brought both new opportunities and problems in the areas of heat management and structural design. A Single Unit (1U) CubeSat structure's design and simulation analysis are presented in this work, with an emphasis on the structure's mechanical and thermal performance during launch and orbit. The CubeSat platform, which is frequently constructed using commercial off-the-shelf (COTS) components, needs robust and lightweight structures that can handle mechanical loads and high temperature changes. In order to conduct mechanical stress loading (MSL) and thermal analysis (TA) on a 3D CubeSat model, this work uses ANSYS in a simulation-driven manner. Geometry modeling, meshing, boundary condition application, and simulation of important space environment elements including microgravity, solar radiation, and launch-induced stresses are all part of the process. The findings
Hussain, Muzammil, Azeem, Naqash, Adeel, Muhammad
A Cooperative AFS/DYC Control for FWDIEV Based on Dissipative Energy Method2026-01-0619To be published on 04/07/2026
To enhance the lateral stability of four-wheel-drive intelligent electric vehicles (FWDIEV) under extreme operating conditions, this paper proposes a cooperative control strategy integrating active front steering (AFS) and direct yaw moment control (DYC) based on dissipative energy method. A nonlinear three-degree-of-freedom vehicle model is established to analyze the evolution of the vehicle state phase trajectory. A quantitative lateral stability index is constructed using dissipative energy to accurately evaluate the vehicle's lateral dynamics. Utilizing dissipative energy and its gradient information, a time-varying stability boundary is defined under dynamic constraints, and adaptive weighting coordination between the AFS and DYC systems is designed to achieve coordinated control of front steering angle and additional yaw moment. A feedforward–model predictive control (FF-MPC) framework is developed, in which a feedforward module generates compensation based on driver intent to
Zhao, Kun, Zhao, Zhiguo, Wang, Yutao, Xia, Xue, Chen, Xi, Hu, Yingjia
Evaluating the Impact of Drag Coefficient Changes on Driving Energy Using a Probability Distribution Model of Yaw Angle Based on Empirical Data from North American Highways2026-01-0615To be published on 04/07/2026
This study estimates the impact on driving energy of differences in aerodynamic characteristics for yaw angle from natural wind during North American Highway mode driving. A previous study[1] clarified the potential to estimate the fuel consumption impact of natural wind by integrating the CD yaw characteristics and yaw angle occurrence frequency. The natural wind was measured on a vehicle while driving a representative North American Highway test course[2]. The driving energy is predicted from the obtained yaw frequency and CD yaw sweep data in a wind tunnel. Measurements were conducted every weekday for 8 hours in 2023, covering 70% of the traffic volume. The validity of the measurement period was evaluated by the deviation from the annual average of wind direction and speed. Since yaw frequency varies depending on the road environment, it is necessary to weight the road environment type frequency when calculating the driving energy. The frequency was calculated using machine
Onishi, Yasuyuki, Nucera, Fortunato, Nichols, Larry, Metka, Matt
Optimization of Road Reference Profiles based on Highway Design for Connected and Automated Vehicles2026-01-0096To be published on 04/07/2026
Advances in Connected and Automated Vehicles (CAVs) have developed a level in which high-definition maps can be used to improve road safety. Data compactness and robustness on road characterization is essential for the proper handling of vehicles under curves. In this paper, an optimization scheme that relates highway-design road curvature and optimal speed of travel is defined to safely navigate through a given road. The scheme is divided in two main steps. First a nonlinear optimization problem, in which curvature profiles are fitted from a model that based on street design standards as per the American Association of State Highway and Transportation Officials (AASHTO). Secondly, the optimized curvature profile is subject to a secondary optimization problem that uses vehicle dynamics for both constraints and objective function derivation. Guidance reference parameters such as curvature and velocity, at different levels of friction are analyzed. Results show that, even in sparse
Jacome, Ricardo Osmar, Stolle, Cody, Grispos, George
Study of suppression properties on traffic waves of periodic vehicular platoons with typical platoon configurations2026-01-0167To be published on 04/07/2026
To investigate the suppression characteristics on traffic waves in the periodic configurations vehicular platoons, this paper analyzes the bandgap properties and frequency transmission characteristics of three typical platoon configurations: predecessor-following (PF), bidirectional (BD), and predecessor-leader-following (PLF). First, dynamic models of periodically structured platoons are developed for each configuration. Then, taking the BD periodic platoon as an example, the spacing error transfer matrix is derived by the lumped mass method, and a bandgap computation approach is formulated based on Bloch’s theorem. Building on this, the frequency transmission characteristics of periodic platoons using PF, BD, and PLF configurations are numerically solved and analyzed. Finally, numerical simulations are conducted to assess the suppression effect of platoons on traffic waves, comparing the bandgap properties and frequency transmission characteristics across different configurations
Yang, Xiujian, Zhuang, Qingyuan, Wu, Ruiqiong, Wang, Shenyi
Operational Feasibility of Battery Swapping in U.S. Transit: A Case Study of King County Metro2026-01-0402To be published on 04/07/2026
Battery swapping technology has emerged as a promising alternative to conventional plug-in charging for electric bus fleets, offering rapid turnaround times and improved vehicle availability. This paper investigates whether battery swapping can be integrated into U.S. public transit bus operations without disrupting existing timetables. A Seattle based public transit agency- King County Metro is taken as a case study in this paper. Using General Transit Feed Specification data from King County Metro, a MATLAB model was built that reconstructs blocks and layovers, extracts dwell-time opportunities, and performs block distance and block time analyses to understand operational rhythms. A mathematical energy model was developed that maps route mileage, efficiency, and layover availability to swap decisions, using a look-ahead rule that defers exchanges whenever the next feasible layover can still be reached while respecting a minimum state-of-charge. The workflow estimates how many swaps
Vadlapatla, Taraka Rishi, Jankord, Gregory, D'Arpino, Matilde
Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior2026-01-0465To be published on 04/07/2026
The transition to sustainable mobility and energy systems is a complex socio-technical challenge, where the success of new technologies and policies hinges on their interaction with human behavior. Traditional models often fail to capture the nuanced, heterogeneous, and adaptive nature of occupant decision-making, particularly in terms of mobility choices and energy use, creating a critical uncertainty gap for system design and policy evaluation. This paper presents a new paradigm to address this gap: a scalable, high-fidelity simulation framework based on Generative Agents. The framework's core innovations are twofold: 1) It leverages Large Language Models (LLMs) to autonomously generate agents with intrinsic personality traits autonomously, enabling a realistic simulation of diverse, human-like responses to environmental, social, and transportation-related cues. 2) It introduces a hierarchical "Prototype-Individual" architecture that makes large-scale simulation of entire communities
Chen, Yongjian, Yang, Zhifeng, Ou, Shiqi(Shawn)
A Comparison of Road Grade Preview Signals from Lidar and Maps2026-01-0026To be published on 04/07/2026
Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering
Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy
Data-Driven Objective Road-Driving Classification For Durable Design Of Vehicles2026-01-0181To be published on 04/07/2026
Durability development in the automotive industry depends on a clear understanding of how road surfaces and driving characteristics affect structural road loads and fatigue. Traditionally, road surface classification has been subjective (e.g., city, highway, rural), and done through driving instrumented vehicles over a small selection of roads. The variations in driving characteristics that are often consequent to the road surface quality is rarely accounted for in designing vehicle level durability tests. This makes it difficult to establish targets for the durability testing that accurately match the wide variations in real-world roads and driving across the market of vehicles. This paper presents a data-driven approach to objectively classify road surface and driving characteristics using metrics derived from existing vehicle and road response metrics like Vibration Dose Value (VDV), International Roughness Index (IRI) and statistical estimates of vehicle speed and acceleration
Shaurya, Shubham, Ramakrishnan, Sankaran, Demiri, Albion, Khapane, Prashant
Look-ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks2026-01-0039To be published on 04/07/2026
Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the
Safder, Ahmad Hussain, Villani, Manfredi, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Advanced Tire Looseness Identification through Algorithmic Gear Error Analysis and Probabilistic Assessment2026-01-0590To be published on 04/07/2026
At present, tire failures directly affect road safety, and the number of incidents caused by them is gradually increasing. Examining tire looseness on time is vital for vehicle safety. Tire-related incidents not only put people in peril but also have a detrimental effect on the economy. Therefore, the goal of this research is to develop a new and effective method for identifying tire looseness. A novel gear error reduction approach, distinct from traditional methods, combines advanced computing and probabilistic analysis. This paper involves three key components: extracting looseness eigenvalues, calculating ring gear errors, and computing the tire loosen probabilities. Gear errors derived from the Kalman filter and adjusted for speed, eigenvalues were calculated, and a tire loosening probability analysis was performed. Real-car trials across speeds and roads confirm its accuracy and reliability. This technology can improve automotive safety and maintenance, reducing accidents, claims
Zhao, Gaoming, Zhang, Zhijie, Liu, Jianjian, Ma, Guangtao, Wang, Zhenfeng, Zhao, Binggen
Research on Attitude Control and Stability of Water-Wading Vehicles2026-01-0627To be published on 04/07/2026
With the intensification of global climate change, the frequency of extreme rainfall has increased, and the number of urban waterlogging and water-crossing sections in the wild has significantly risen. The scenarios where vehicles encounter complex water-crossing road conditions are also on the rise, which poses unprecedented challenges to the passability and safety of modern vehicles. This article conducts a study on the problem of attitude instability that occurs when vehicles are driving in water. Traditional vehicles have insufficient passability when wading through water. Their passive suspension systems cannot adaptively adjust when wading through water, resulting in insufficient vehicle stability and a high risk of serious accidents such as "water skidding" or even overturning. The safety of driving through water cannot be guaranteed. In this regard, this paper first establishes a dynamic model of a wading vehicle body considering the effect of buoyancy. On this basis, it
Li, LiuYuanHeng, Tan, Gangfeng, Dong, Chenchen, Fayzullayevich, Jamshid, Han, YiFei
Aerodynamic Proximity Effects Between the DrivAer and AeroSUV Standard Models, Part 1 – Same-Lane Interactions2026-01-0605To be published on 04/07/2026
Road-vehicle platooning is traditionally defined as close-distance longitudinal following in laterally-aligned formation. In this paper, the effects of aerodynamic interactions on the drag of a two-vehicle system are examined by considering the influence of separation distance, cross winds, vehicle size and shape. Testing was undertaken at 30% scale in a large wind tunnel with road-representative freestream turbulence. Separation distances of 0.5, 1.0, and 2.0 vehicle lengths were examined over a range of yaw angles between ±15deg. A highlight of the current study is the characterization of platoon drag-reduction benefits for different sizes and shapes of the lead and follower models, by using a DrivAer model and an AeroSUV model, each with slant-back (Notchback or Fastback) and square-back (Estateback) variants, providing four distinct model pairings. Drag reduction for the lead model appears to be affected mainly by the size of the follower vehicle, while the follower model shows a
McAuliffe, Brian, Ghorbanishohrat, Faegheh
An Updated Correlation Study between the Full Size Wind Tunnels of General Motors, Ford Motor Company, and FCA US LLC2026-01-0612To be published on 04/07/2026
As automotive aerodynamic testing facilities evolve to capture more real-world behavior, updating the correlation between old and new technologies is essential. Recently, the three-member consortium of the United States Council for Automotive Research (USCAR) - FCA US LLC, Ford Motor Company, and General Motors - transitioned from full-size static ground plane facilities to 5-belt moving ground plane wind tunnel facilities. The primary objective of this study was to update the correlation data sets to maintain consistent and robust data sharing among companies, which is the cornerstone of USCAR efforts. To achieve this, a set of updated correlation data sets were calculated to replace the original correlation study results from 2008. Additionally, the methodology for applying correlation equations was revised from using averaged wind tunnel data to employing direct wind tunnel-to-wind tunnel correlation equations. In a two-phase correlation effort conducted in 2022 and 2025, the three
Nastov, Alexander, Lounsberry, Todd, Madin, Trevor, Langmeyer, Gregory, Fadler, Gregory, Skinner, Shaun, Horton, Damien
A Study on Vehicle Cornering Stiffness Estimation Based on Recursive Least Squares Method2026-01-0630To be published on 04/07/2026
This paper investigates a vehicle cornering stiffness estimation method based on recursive least squares (RLS), aiming to enhance automotive active control capabilities. Conventional parameter identification schemes reliant on onboard sensors are constrained by sensing range and cost limitations. This study explores an estimation algorithm utilizing generic motion signals, derives the theoretical relationship between yaw rate response and yaw stiffness, and designs an RLS approach for online identification of relevant model parameters. In a simulation environment, applying an ideal steering angle excitation demonstrated that the RLS algorithm converges within approximately one second and successfully estimates the cornering stiffness with an error of less than 5% relative to the set true value. Results indicate that under known vehicle basic parameters and ideal input conditions, the proposed algorithm can effectively complete cornering stiffness estimation. This work establishes an
Lang, Tao, Tan, Gangfeng, duan, xianqi, Shen, Leiming, Liang, Shuang
A Real-Time Repositioning Strategy for SAEV Fleets Using a Simulation-Informed Deep Learning Model2026-01-0035To be published on 04/07/2026
Shared Autonomous Electric Vehicles (SAEVs) can enhance urban mobility and efficiency. However, their operational performance is often hindered by the spatio-temporal imbalance between vehicle supply and passenger demand, leading to long wait times. This paper develops a novel repositioning framework where a lightweight CNN, informed by computationally intensive multi-agent simulations, enables real-time strategy deployment. The results show that: (1) An optimized repositioning policy, calibrated via multi-agent simulation, effectively cuts the mean passenger waiting time from 12.0 to 3.0 minutes (a 75% reduction). (2) A lightweight CNN surrogate model enables real-time deployment, reducing the policy computation time from ~4 hours to ~5 minutes (>98% faster). (3) The deep learning surrogate achieves this speed with a negligible performance trade-off, increasing the waiting time by only 0.156 minutes (4.9%) compared to the full optimization.
Shang, Kai, Wang, Ning
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-0097To be published on 04/07/2026
By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic, agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human
Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose
Data-driven Study on Chassis Suspension Performance Degradation2026-01-0582To be published on 04/07/2026
The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Under severe operating conditions, health monitoring of chassis suspension mechanisms can detect potential problems in time, thereby reducing safety risks and optimizing maintenance costs. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve real-time monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a semi-vehicle suspension model is constructed to simulate the vehicle's degraded state under bumpy road conditions, generating a dataset of key suspension parameters. Subsequently, a DNN model comprising three hidden layers is developed to assess suspension performance degradation. To optimize model performance, the effects of different numbers of neurons and hidden layers on model accuracy are explored. Experimental
Liao, Yinsheng, Lei, Yisong, Su, Ailin, Wang, Zhenfeng
Evaluating Cloud Platforms for Real-Time Cooperative Perception Systems Using RSU Cameras: A Comparative Study of Google Colab, Azure, and AWS2026-01-0018To be published on 04/07/2026
This paper presents a comparative study of three widely used cloud platforms, Google Colab, Microsoft Azure, and Amazon Web Services (AWS), for running a real-time cooperative perception system based on roadside unit (RSU) cameras. The goal is to evaluate the performance, scalability, and cost-efficiency of each platform when handling high-volume video data for object detection, a key task in autonomous driving. A unified perception pipeline using the YOLOv8 Small model was deployed on all platforms, with the same dataset and settings to ensure fair comparison. The evaluation focused on key metrics such as latency, frame processing rate, scalability, and cost per frame. The results show that Google Colab is a cost-effective starting point but has limitations in uptime and scalability. Azure offers stable performance and balanced cost, making it suitable for medium-scale applications. AWS delivers the best scalability and speed but at a higher cost. This study provides practical
Alkharabsheh, Ekhlass, Alawneh, Shadi, Rawashdeh, Osamah
Bias and uncertainty in the time, position, and speed data of consumer-grade GPS devices2026-01-0544To be published on 04/07/2026
The goal of this study is to quantify the accuracy and uncertainty of position and speed measurements acquired by a wide range of consumer-grade GPS-enabled devices while road cycling. We acquired position and speed data simultaneously from 13 consumer devices (e.g., phones, watches, bicycle computers) at their maximum sampling rate (typically 1 Hz) during 10 hours (thirty 20 minute sessions) and 150 km of road cycling in a suburban environment. The position data from these devices were compared to real-time kinematic (RTK) data acquired using a differential GPS system and the speed data from these devices were compared to speed data acquired with high-resolution wheel rotation sensors (23,040 pulses per revolution) synchronized with the RTK data. Based on these comparisons, we generated probability distributions for the errors in the position and speed measured by each of the consumer devices. This study provides foundational data needed to quantify the accuracy and uncertainty across
Booth, Gabrielle R., Mitchell, Alan L., Siegmund, Gunter P.
Pre-Impact Seat Rotation for Mitigating Lumbar Injury in Flying Car Crash2026-01-0558To be published on 04/07/2026
Flying cars have already been used in tourism, firefighting, and logistics, and might be soon used for short-distance commute. However, the lumbar spine injury risks in flying car crash accidents have raised safety concerns. This is because the crash load of a flying car is largely aligned with the orientation of the occupant’s spine. This study introduces a countermeasure of actively adjusting seat posture for mitigating lumbar injury in crash events. A flying car crash usually has a few seconds of warning time before collision to ground. The pre-impact warning time is enough to rotate the seat and occupant together using seat motors. Posteriorly rotating seat can alter the angle between the crash load and the spinal axis, thereby reducing lumbar injury risk. Using numerical simulations, the 30g deceleration pulse defined in SAE-AS-8049 was applied to seat of flying car. The THUMS (Total Human Model for Safety) human body model was used to model occupant, sitting in a typical vehicle
Zhuang, Ziao, Puyuan, Tan, Shen, Wenxuan, Zhou, Qing, Gu, Gongyao
Introduction to Catesby Aero Research Facility, CARF, the coast-down tunnel proving ground.2026-01-0611To be published on 04/07/2026
This paper reports on the Catesby Aero Research Facility (CARF), which began commercial use in 2019, and provides an overview of the facility and related technologies, particularly details on the vehicle-mounted component force measurement devices. CARF is a proving ground that was renovated from a former railway tunnel approximately 2.74km long and is now covered with high-quality tarmac. The road surface quality was designed to be at least as good as that of SUBARU's proving ground, and the standards were achieved using British construction technology. The course is almost straight, but there is a slight incline. The course specifications are: blockage 40m2, road width 6m (maximum 8.4m), flatness σ<0.5mm, and gradient 0.57%.In particular, compared to outdoor coastdown trucks, its overall length allows for continuous measurement right up to the stop, giving it a significant advantage in repeatability.At the same time, Subaru's newly developed six-component force sensor built into the
Shimoyama, Hiroshi
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-0165To be published on 04/07/2026
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback
Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin
Survivability, Lethality, and Mobility Requirements of Military Vehicles Conducting Future Wet Gap Crossings2026-01-0264To be published on 04/07/2026
Wet gap crossings, which involve moving military forces across rivers and other water obstacles, are among the most difficult maneuvers that units can perform. These operations are complicated by choke points, fast-flowing water, and the exposure of forces to enemy fire. Despite these challenges, wet gap crossings remain critical to battlefield success, particularly during large-scale combat operations. Consequently, military vehicles must be designed with these operations in mind. This study examines doctrinal approaches to wet gap crossings and the trade-offs between mobility, survivability, and lethality in relation to fording, floating, rafting, and bridging. The analysis incorporates open-source data from the Institute for the Study of War, which provides daily updates on the Russia-Ukraine conflict, and Oryxspioenkop, which collates open-source images of Russian and Ukrainian battle-damaged equipment. This data is used to assess additional challenges encountered during wet gap
Lynch, Benjamin, Dosan, Logan, Mittal, Vikram
Optimal Control Analysis of Minimum-Time Maneuvers for Vehicles Recovering from Instability2026-01-0218To be published on 04/07/2026
Vehicles may enter highly unstable dynamic states due to lateral collisions, sudden loss of grip, or extreme steering disturbances. When such instability arises in congested road sections where obstacle avoidance is required, the safety risk to both the ego vehicle and surrounding traffic escalates significantly. In such scenarios, the vehicle must not only regain stability but also navigate the roadway in the shortest feasible time to prevent secondary collisions. This paper investigates the minimum-time maneuver of a vehicle starting from an unstable dynamic condition and constrained to travel within prescribed road boundaries. A single-track vehicle model with combined-slip nonlinear tire model is employed to capture the vehicle dynamics under high slip conditions. Phase-plane analysis is conducted to reveal how control inputs reshape the system’s vector field and influence the possibility and speed of stability recovery. An optimal control problem is formulated to compute the
Leng, Jiatong, Yu, Liangyao, Wang, Yongxin, You, Weijie, Li, Ziang, Jin, Zhipeng
Energy-Aware Traffic Signal Optimization for Sustainable Urban Networks Using Valid Action Policies2026-01-0462To be published on 04/07/2026
Optimizing signal control across urban networks is crucial for reducing energy consumption and improving traffic efficiency. Advances in Vehicle-to-Everything (V2X) technologies have enabled deep reinforcement learning (DRL) to demonstrate significant promise in traffic signal control. However, many existing studies focus primarily on agent performance while neglecting practical operational constraints, which can lead to fairness issues in traffic control. Moreover, prior work often treats energy optimization as a by-product of efficiency. To address these gaps, this study proposes a multi-agent DRL framework to mitigate peak-hour congestion and reduce excessive emissions in urban networks. Building on this framework, a variant of Independent Proximal Policy Optimization (IPPO), termed Global State Independent PPO (GS-IPPO), is developed to enhance IPPO's coordination capability. To further improve practical applicability and fairness, this study incorporates constraints imposed by the
Qian, Wangrui, Jiang, Shuyan, Qi, Hao, Ou, Shiqi(Shawn)
Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks2026-01-0029To be published on 04/07/2026
Traffic roundabouts, as complex and safety-critical road scenarios, present significant challenges for autonomous vehicles. In particular, predicting and managing dilemma zone (DZ) encounters at roundabout intersections remains a pivotal concern. This paper introduces an AI-driven system that leverages advanced trajectory forecasting to anticipate DZ events, specifically within traffic roundabouts. At the core of our framework is a modular, graph-structured recurrent architecture powered by graph neural networks (GNNs). By modeling agent interactions as a dynamic graph, our approach integrates heterogeneous data sources, including semantic maps, while capturing agent dynamics with high fidelity. This GNN-based forecasting model enables accurate prediction of DZ events and supports safer, data-driven traffic management decisions for both autonomous and human-driven vehicles. We validate our system on a real-world dataset of roundabout intersections, where it achieves high precision with
Lu, Duo, Farhadi, Mohammad, Satish, Manthan, Yang, Yezhou, Chakravarthi, Bharatesh
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-0113To be published on 02/12/2026
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless
Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan
Development of Plug-in Hybrid Motorbike and Analysis towards Sustainable Urban Mobility2026-28-0111To be published on 02/12/2026
The transportation system is one major catalyst to urban ecological imbalance. In developing countries, two-wheelers are considered a major mode of urban personal transportation because of their compactness, easy maneuver in heavy traffic and good fuel efficiency. In India, middle and lower middle-class people prefer to choose two wheelers and these vehicles are dominantly fueled by gasoline. Although, the energy consumption by a two-wheeler is comparatively less than that of a four-wheeler, they use about 60% of the nation's petroleum for on-road vehicles and the impact on urban air quality and climatic change is significantly high. This high proportion of gasoline utilization and emission contribution by two wheelers in cities demand greater attention as a way to improve urban air quality and near-term energy sustainability. Electrification of two-wheelers through the application of a plug-in hybrid idea is a promising solution. A plug-in hybrid motorbike was developed by putting
Kannan, Prashanth, Shaik, Amjad, Talluri, Srinivasa Rao
SAE TOMORROW TODAY - SAE: Where Meaningful Relationships Take Shape135531/29/2026
Curious how SAE can open doors in the automotive industry and beyond? Listen in as we sit down with Dean Case, Membership Director, SAE SoCal, whose 45-year journey in the automotive industry is packed with stories of racing, engineering, and community building. From joining SAE as a Cal Poly student to shaping the future of motorsports and electric vehicles at Mazda, Ford, and Nissan, learn how SAE International became a springboard to building lifelong connections and uncovering new opportunities. Whether you're a student, engineering professional, or automotive enthusiast, this conversation is packed with inspiration, practical advice, and proof that SAE provides a firm foundation for meaningful relationships that last. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders
Patterson, Lori
SAE TOMORROW TODAY - Advocating for Growth in the U.S. Auto Industry135521/27/2026
From tariffs to trade policies, today's automakers must respond to a variety of pressures to remain competitive in a changing manufacturing landscape. As the voice of international automakers in the United States, Autos Drive America is strengthening the automotive manufacturing by advocating for trade and investment policies that expand jobs and support economic growth in America. Listen in as we sit down with Jennifer Safavian, President & CEO, to discuss why a strong U.S. auto industry is vital for the nation's economy and how Autos Drive America is supporting integrated supply chains that keep American manufacturing competitive on the global stage. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on
Patterson, Lori
Path Planning and Control Strategies for a Semi-Autonomous Light Commercial Vehicle Navigating and Avoiding Static Obstacles in Urban Environments2026-26-01341/16/2026
This paper presents the design and implementation of a Semi-Autonomous Light Commercial Vehicle (LCV) capable of following a person while performing obstacle avoidance in urban and controlled environments. The LCV leverages its onboard 360-degree view camera, RTK-GNSS, Ultrasonic sensors, and algorithms to independently navigate the environment, avoiding obstacles and maintaining a safe distance from the person it is following. The path planning algorithm described here generates a secondary lateral path originating from the primary driving path to navigate around static obstacles. A Behavior Planner is utilized to decide when to generate the path and avoid obstacles. The primary objective is to ensure safe navigation in environments where static obstacles are prevalent. The LCV's path tracking is achieved using a combination of Pure Pursuit and Proportional-Integral (PI) controllers. The Pure Pursuit controller is utilized as lateral control to follow the generated path, ensuring
Ayyappan, Vimal Raj, Dhanopia, Rashmi, Ali, Ashpak, N, Ragesh, Sato, Hiromitsu
Optimizing Suspension Joint Reliability: Overcoming Bolt Loosening and Torque Variability in Automotive Production2026-26-04921/16/2026
In modern automotive manufacturing, ensuring the integrity of suspension joints under real-world driving conditions is a critical aspect of vehicle safety and performance. These joints endure substantial transverse loads and large vibrations due to irregular road surfaces, dynamic maneuvers, and varying environmental factors. As a result, bolt loosening becomes a significant concern, compromising joint integrity and overall vehicle reliability. This paper delves into the challenges associated with maintaining joint integrity, specifically focusing on pre-load determination, torque application, and production-related issues. The pre-load generated during torquing is the primary factor that ensures a suspension joint remains securely fastened under dynamic road conditions. This pre-load is derived using road load data acquisition (RLDA) inputs, which capture the forces acting on the joint during actual driving scenarios. RLDA inputs provide critical insights into the forces experienced
Kumar, Sabeesh, Vasant Kumar, Jesse Daniel, Mishra, Harshit, Senthil Raja, T, Nayak, Bhargav, M, Sudhan, Namani, Prasad, Vibhute, Shekhar
Quantifying the Energy Consumption of HVAC Operation and Its Impact on the Range of Electric Buses Across Diverse Climatic Conditions2026-26-02041/16/2026
Electric buses (e-buses) are essential to sustainable public transport, but their real-world efficiency and range are heavily affected by auxiliary systems, particularly the Heating, Ventilation, and Air Conditioning (HVAC) system. This study investigates how ambient temperature variations and HVAC loads influence energy consumption, range, and efficiency in e-buses operating under diverse climatic conditions. The methodology combines field data collection from urban e-buses across seasons—including extreme summer and winter—with controlled laboratory testing. Field measurements included ambient temperature, HVAC demand, vehicle speed, state of charge (SOC) variation, and energy consumption. These inputs were used to develop real-world duty cycles, replicating actual thermal loads, passenger profiles, idling periods, and driving patterns. In the laboratory, these cycles were simulated using a chassis dynamometer and environmental chamber, with HVAC systems tested at controlled ambient
Vishe, Prashant, Dalela, Saurabh, Saraswat, Shubham, Joshi, Madhusudan
Decision-Fatigue Mitigation in Automotive HMI System2026-26-06521/16/2026
With the advent of digital displays in driver cabins in commercial vehicles, drivers are being offered many features that convey some useful or critical information to drivers or prompt the driver to act. Due to the availability of a vast number of features, drivers face decision fatigue in choosing the appropriate features. Many are unaware of all available functionalities displayed in the Human Machine Interface (HMI) System, leading to a bare minimum usage or complete neglect of helpful features. This not only affects driving efficiency but also increases cognitive load, especially in complex driving scenarios. To alleviate the fatigue faced by drivers and to reduce the induced lethargy to choose appropriate features, we propose an AI driven recommendation agent/system that helps the driver choose the features. Instead of manually choosing between multiple settings, the driver can simply activate the recommendation mode, allowing the system to optimize selections dynamically. The
K, Sunil, Dhoot, Disha
Multi-Band Frequency Approach to Correlate Proving Ground and Road Simulator for an Electric Two-Wheeler2026-26-05561/16/2026
Real-world usage subjects two-wheelers to complex and varying dynamic loads, necessitating early-stage durability validation to ensure robust product development. Conducting a full life-cycle durability testing on proving grounds is time-consuming, extremely difficult for the riders involved, and costly, which is why accelerated testing using rigs such as the road simulator system have become a preferred approach. The use of road simulators necessitates, accurately measured inputs and precise simulation to ensure proper actuation of the rig, thereby enabling realistic representation of road undulations. This paper covers two important aspects essential for achieving an accurate and clear representation of road simulation in a 4-DOF road simulator, encompassing both longitudinal and vertical simulations at the front and rear of the vehicle. The first aspect involves the development of an instrumentation strategy for the two-wheeler, with careful identification of directionally sensitive
Ganju, Shubham, V, Vijayamirtharaj, Prasad, Sathish, R S, Mahenthran
Design Optimization of Jounce Bumper Support Ring Based on RWUP Loading2026-26-05141/16/2026
Designing and manufacturing a support ring (POM ring -Polyoxymethylene ring) for a MacPherson strut suspension system brings unique set of challenges due to the high-performance and durability demands for Indian road application. Support ring along with the jounce bumper used in the shock absorber is designed to absorb the strong shock coming from the road inputs when suspension travel reached to the maximum limit. thereby absorbing the impact energy and preventing it from transferring it to the body. A bump stopper for a suspension of a vehicle is made of poly urethane (PU) material and is surrounded by a support ring or POM ring made up of Polyoxymethylene material. The bump stopper deflects into bellow shape during the absorption of impact energy. In the present paper, the authors have demonstrated the key challenges experienced in successfully designing the support ring post initial failure experienced in the validation phase which was unprecedented. The authors detail the failure
Koritala, Ashok Kumar, Malekar, Amit, Kulkarni, Purushottam, S, Sivashankar, Mishra, Harshit, Ganesh, Mohan Selvakumar, Patnala, Avinash, J, Ramkumar, Nayak, Bhargav, M, Sudhan
Revolutionizing Urban Mobility in India: The Emergence of the 2-3 Wheel Combi Vehicle and the L2-5 Regulatory Framework2026-26-01871/16/2026
Growing population in Indian cities has led to packed roads. People need a quick option to commute for both personal trips and business needs. The 2-3 Wheel Combination Vehicle is a new, modular solution that switches between a two-wheeler (2W) and a three-wheeler (3W). Hero has designed SURGE S32 to be a sustainable and flexible transportation option. It is world’s first class changing vehicle. The idea is to use a single vehicle for zipping through city traffic, making deliveries, or earning an income. Manufactured to deal with the challenges of modern life, this dual-battery convertible vehicle can easily transform from a two-wheeler to a three-wheeler and vice versa within three minutes. The Surge S32 is a versatile vehicle that replaces the need for multiple specialised vehicles. By lowering the number of vehicles on the road, it decreases road congestion, reduces emissions, and improves livelihoods. It powers by electricity, ensuring sustainability in all aspects. The current
Ali Khan, Feroz, Gupta, Eshan
GTSRB Shield: A Novel Machine Learning Approach to Anomaly Detection in German Traffic Signs Recognition Benchmark (GTSRB) Dataset2026-26-06111/16/2026
The escalating dependence of Autonomous Vehicles on Intelligent Transportation Systems (ITS) has highlighted the imperative for comprehensive security protocols to safeguard such vehicles against cyber threats. Intrusion Detection Systems (IDS’s) are pivotal in ensuring the protection of these systems by detecting and alleviating unauthorized access and nefarious activities. The German Traffic Sign Recognition Benchmark (GTSRB) database, which encompasses an extensive compilation of traffic sign imagery, functions as a vital asset for the advancement of machine learning-based IDS. This research elucidates an intrusion detection system (IDS) that employs machine learning algorithms to scrutinize the GTSRB database. The proposed IDS emphasize the preprocessing of the GTSRB dataset to extricate pertinent features that can be employed for the training of machine learning models. Research also focuses on model development with machine learning algorithms to classify traffic signs and
Patil, Kamalesh, Akbar Badusha, A., Jadhav, Savitri, Gunale, Kishanprasad
Investigation and Reduction of Road Noise due to Transient Road Inputs in Electric Vehicle2026-26-03581/16/2026
Higher road noise is perceived in the cabin when the test vehicle encounters road irregularities like bump or pothole in the public roads. The transfer of transient road inputs inside the body caused objectionable cabin noise. Measurements are conducted at different road surfaces to identify the patch where the objective data well correlated with the noise measured at the public road. Wavelet analysis is carried out to identify the frequency zones since the events are transient in nature. TPA is carried out in time domain to identify the nature of the noise and the dominant path through which the transient road forces are transferring inside the body. Based on the outcome of TPA, various countermeasures like reduction of dynamic stiffness of suspension bushes, TMDs on the path are proposed to reduce the structure borne noise. Criteria which need to be considered for reduction of cabin noise due to transient road inputs is also discussed.
S, Nataraja Moorthy, Rao, Manchi, Selvam, Ebinezer, Raghavendran, Prasath
A Comprehensive Study: The Potential of Wireless Charging in Future E Mobility2026-26-01951/16/2026
Electric mobility is no longer a distant vision, it is a global imperative in the journey of fight against the climate change and the urban pollution. Yet, despite of explosive growth in the electric vehicle adoptions, a major bottleneck remains which is efficient and convenient charging. The current reliance on physical plug in charging station creates inconvenient, time consuming experience and also faces significant technical and economic challenges those threaten to stall the smooth clean transportation revolution. Without innovation in how we recharge our vehicle the promise of electric mobility appears under threat which is undermined by less efficient, less compatible, and infrastructure hurdles. Wireless charging technology stand out as the game changing breakthrough poised to tackle these all critical problems head on. By enabling the effortless, cable-free charging system across the wide spectrum of electric vehicles, from the personal cars to the public transport fleets and
Jain, Gaurav, Premlal, P, Pathak, Rahul, Gore, Pandurang
Real-Time Helmet Detection and Closed-Loop Enforcement System for Two Wheelers2026-26-06501/16/2026
Asian countries capture a significant share of global two-wheeler usage, with India consistently ranking among the top three countries. 2 wheelers are a significant portion of road traffic and contribute heavily to the national burden of road fatalities. Despite regulatory mandates, helmet non-compliance remains widespread due to limited enforcement reach and behavioural inertia. The current strategies for enforcement, such as traffic policing or external camera-based surveillance, are reactive, infrastructure-dependent, are ineffective at scale. To address these limitations, we propose system that will detect if the user is wearing the helmet. The system is designed and packaged to be integrated into the 2-wheeler directly and then execute functions in real-time for helmet noncompliance. The software algorithm is an AI-powered, vision-based system that leverages deep learning techniques for helmet detection. This model is enforced with a custombuilt dataset accommodating cultural and
Kandimalla, Om Mahalakshmi, Shah, Ravindra, Karle, Ujjwala
Generative AI Based Methodology for Road Profile Mix Creation2026-26-06741/16/2026
The acquisition of road profile data is crucial for various automotive testing applications, including vehicle dynamics analysis, chassis endurance tests, and simulation of vehicle-road interactions. This is necessary for conducting virtual tests to accelerate research and development processes and can significantly reduce testing costs. However, most of the on-road measurements lack comprehensive and relevant road profile data. Conducting on-road trials to acquire this data is a laborious and time-consuming process, often impeded by logistical and environmental challenges. This research proposes a generative AI-based methodology for creating diverse and realistic road profile mixes from the existing on-road dataset of front axle displacement and road profile measured with a laser sensor. By leveraging advanced machine learning techniques, the proposed approach seeks to generate synthetic road profiles that accurately reflect real-world conditions, thereby reducing the dependency on
Rajappan, Dinesh Kumar, Venkatesh, Anirudh Anand, R, Suresh, N, Gopi Kannan
Assessment of Cybersecurity Risks in State-Operated Connected Electric Passenger Buses in India2026-26-06101/16/2026
State Transport Units (STUs) are increasingly using electric buses (EVs) as a result of India's quick shift to sustainable mobility. Although there are many operational and environmental benefits to this development, like lower fuel prices, fewer greenhouse gas emissions, and quieter urban transportation, there are also serious cybersecurity dangers. The attack surface for potential cyber threats is expanded by the integration of connected technologies, such as cloud-based fleet management, real-time monitoring, and vehicle telematics. Although these systems make fleet operations smarter and more efficient, they are intrinsically susceptible to remote manipulation, data breaches, and unwanted access. This study looks on cybersecurity flaws unique to connected passenger electric vehicles (EVs) that run on India's public transit system. Electric vehicle supply equipment (EVSE), telematics control units (TCUs), over-the-air (OTA) update systems, and in-car networks (such as the Controller
Mokhare, Devendra Ashok
Vehicle-to-Vehicle Negotiation Via Manoeuvre Coordination Messages for Cooperative Cut-in Scenarios2026-26-02741/16/2026
The automotive industry is rapidly extending the capabilities of automated systems by incorporating connectivity and cooperation features that enable real-time information exchange between vehicles and road infrastructure. Within the Connected, Cooperative, and Automated Mobility (CCAM) framework, Vehicle-to-Vehicle (V2V) communication is expected to play a key role in improving road safety, traffic efficiency, and driving comfort. This work addresses a practical implementation of the standardized Manoeuvre Coordination Messages (MCMs), as defined in the ongoing ETSI standard (ETSI TS 103 561). The proposed approach is demonstrated through a cooperative cut-in use case in which two vehicles negotiate a lane change manoeuvre. In the considered scenario, the ego vehicle, driven by a Highway Pilot (HWP) system, receives the intention to cut-in from a neighbouring cooperative vehicle through an MCM. In response, the ego vehicle adapts its behaviour by decelerating to generate a safe
Leiva Ricart, Gisela, Domingo Mateu, Bernat
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