Browse Topic: Autonomous vehicles

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Physics-Constrained Procedural Generation of Synthetic Environments for the Robust Validation of Autonomous Maneuver and Logistics2026-01-75499/22/2026
The validation of Autonomous Ground Vehicles (AGVs) and intelligent logistics planners is frequently compromised by the ”Sim-to-Real” gap, where simulation environments fail to replicate the physical friction of operational deployment. Ideally, valid test cases must enforce strict mobility constraints and impose realistic sustainment penalties; however, many current generation tools rely on idealized terrain interactions and infinite-resource assumptions. We present a real-time procedural framework designed to generate high-friction validation environments that stress-test the robustness of the System Under Test (SUT). The architecture integrates gradient-based terrain analysis with a stochastic contested logistics model. It ingests synthetic heightmaps to precompute mobility corridors, ensuring that every generated evaluation episode adheres to vehicle-specific traversability limits. Simultaneously, a logistics kernel enforces fuel consumption scaled by terrain gradients and models supply chain interdiction as a parameterized Bernoulli process. We validate this framework through a ”Digital Twin” methodology, demonstrating that terrain-aware generation eliminates invalid initialization states (0% mobility violations) while the logistics model induces operationally relevant failure modes in the SUT. This unclassified, open-architecture approach supports DoD Verification, Validation, and Accreditation (VV&A) requirements by providing deterministic, reproducible edge cases for autonomous system evaluation.
Soykan, Bulent, Rabadi, Ghaith, Bochenek, Grace, Paul, Victor J.
Multimodal Digital-Twin Simulations for Off-Road Autonomous Vehicles2026-01-75509/22/2026
Research in autonomous driving has largely focused on structured, on-road environments in densely populated urban areas, leaving off-road autonomy comparatively underexplored despite its importance for applications such as search and rescue, agriculture, and defense. Progress in developing solutions for off-road autonomy is heavily constrained by the high cost, safety risks, and limited coverage of real-world off-road data collection, particularly for rare terrain-induced failure cases. To address these challenges, we introduce a large-scale simulation platform for off-road autonomous driving generated in the Unity3D engine. Our simulator provides indefinite data from multiple RGB cameras, LiDAR, GPS, and IMU sensors to support research in long-duration applications. Our platform also facilitates research in risk-aware planning through dynamic weather, lighting, and annotations for sequence-level failures like collisions and weather-induced sensor blockages. By combining long-duration temporal coverage, multimodal sensing, and procedurally generated terrain diversity, our simulator facilitates progress for systematic evaluation of learning-based autonomous driving models in unstructured, safety-critical environments.
Ross, Timothy, Boone, Julia, Afghah, Fatemeh
Clemson Off-Road Dataset (CORD): A Multimodal Benchmark for Autonomy beyond the Flat-World2026-01-75189/22/2026
While autonomous perception has matured within the structured confines of urban roadways, it remains brittle when confronting the chaotic, non-rigid terrain of the natural world. This paper introduces the Clemson Off-Road Dataset, a high-fidelity, multimodal dataset engineered to bridge this gap by challenging standard “flat-world” assumptions. Featuring 2.90 TB of sensor data, the dataset captures a diverse spectrum of unstructured environments, ranging from the transitional trails of CU-ICAR and the day/night lighting dynamics of TN3 to the unstructured wilderness of Camp Daniels and the novel coastal scenery of Edisto Island. Distinguishing itself from existing forest-centric benchmarks, the Clemson Dataset provides a first-of-its-kind focus on coastal data, featuring unique adversarial conditions such as extreme solar glare, loose sand, and shifting tide lines. The data is collected aboard a Polaris RZR Pro R 4, a high-performance platform integrated with a sensor suite designed to perceive physics beyond geometry. Alongside 360° HD camera coverage, 3D LiDAR, and Radar, we integrate Cubert Ultris Hyperspectral imaging and Prophesee EVK4 Event-based vision to enable material classification and high-dynamic-range motion tracking. To overcome the bottleneck in ground truth generation, we used our “AI LabelMate,” a context-aware semi-automated annotation agent that fuses Vision-Language Models (Florence-2) with SAM2 to generate 6331 pixel-perfect annotated frames using a specialized off-road ontology and a human-in-the-loop pipeline. We establish performance baselines using Oneformer for semantic segmentation and used SalsaNext for lidar point clouds labelling. Available in both raw ROS2 bag and extracted standard formats, this Dataset serves as a pivotal testing ground for the next generation of robust autonomous systems.The dataset of this paper is available upon request to the Virtual Prototyping of Autonomy-Enabled Ground Systems (VIPR-GS) Center.
Patil, Ashish, Gupta, Prakhar, Bhosale, Mayuresh, Mukwaya, Arthur, Jegede, Akinbobola, Mikulski, Dariusz, Mwakalonge, Judith, Jia, Yunyi
Hybridnav: A Hybrid Approach to Zero-Shot Object Detection with Adaptive Exploration for Autonomous Reconnaissance2026-01-75179/22/2026
Autonomous reconnaissance in unknown or contested environments demands robust perception systems capable of identifying diverse objects without prior training data. This paper presents HybridNAV, a hybrid framework that combines multiple foundation models with an adaptive navigation system for zero-shot object detection and autonomous exploration. Unlike monolithic detection models, HybridNAV’s multi-model fusion achieves balanced precision (0.60) and recall (0.58) with a macro F-score of 0.59, representing a 24% improvement over single-model baselines. The adaptive navigation system reduces scan time by 25% and path length by 30% compared to static waypoint approaches, while operating in real-time at 3.2 Hz with sub-100 msec latency on resource-constrained hardware. All processing is performed locally on the robotic platform, eliminating reliance on external communication infrastructure—a critical requirement for operations in communication-denied environments. We evaluate HybridNAV in both simulated indoor scenes and physical robot trials, demonstrating its effectiveness for intelligence gathering in unknown environments.
Indurthi, Hemanth, Martinson, Eric
Bullfrog: AI Powered Counter Unmanned Aerial System2026-01-75199/22/2026
The proliferation of small unmanned aircraft systems (sUAS) presents an asymmetric threat to ground maneuver forces operating in contested and gray-zone environments. The Bullfrog Autonomous Weapon Station (AWS) addresses this operational gap through a passive, AI-powered counter-UAS system employing computer vision and machine learning for autonomous detection, tracking, classification, and engagement. Field testing at Technology Readiness Experimentation (T-REX) 26-1 demonstrated 100% probability of defeat against Group 1 UAS targets with a mean engagement time of 6 seconds and 10 rounds per kill at ranges exceeding 160 meters. Operating in both autonomous and human-in-the-loop modes, Bullfrog achieved 99.45% operational availability while leveraging service-common M240B weapons and Modular Open Systems Architecture for rapid integration with Joint All-Domain Command and Control (JADC2) networks. At $300,000 per unit with $10 cost-per-engagement, Bullfrog demonstrates operational relevance, speed-to-field, and alignment with Army and Marine Corps autonomy priorities.
Cunningham, Jason, Clark, Alex
AI-Driven Predictive Sonification for Human-on-the-Loop Ground Vehicle Autonomy2026-01-75039/22/2026
Ground vehicle autonomy increasingly depends on human-on-the-loop (HOTL) supervision, yet supervisors are often overloaded by visual interfaces that can obscure emerging risks. This paper presents an AI-driven predictive sonification architecture that converts short-horizon forecasts of platoon behavior into structured auditory cues for supervisory monitoring. A forecasting engine predicts future vehicle interaction states and evaluates predicted and active violations to generate a composite risk indicator. When risk exceeds defined thresholds, a sonification module conveys risk magnitude and trajectory through changes in pitch, loudness, modulation, and spatial panning. The paper describes the system architecture, sonification design, operational use cases, and a planned human-subject evaluation. The proposed framework is intended to improve early awareness of emerging instability and support more timely supervisory intervention.
Plotzke, Zachary R., Mohammadi, Alireza, Cheung, Calvin M.
Development of a UAV Platform for Remote Inspection and Visualization2026-01-75119/22/2026
This paper details the development of an intelligence and inspection platform consisting of an attritable sub-250g UAV, a ground control station, and a visualization interface for users. The UAV architecture combines onboard obstacle detection and avoidance along with simultaneous localization and mapping to have full autonomous navigation inside of complicated GPS-denied environments. The ROS 2-to-Unreal Engine data pipeline allows for sensor fusion, data cleansing, and initial analysis as well as creation of a high-fidelity real-time 3D digital twin. The visualization interface allows users to easily identify critical features and turn data into intelligence to support decision making by soldiers and first responders.
Lee, Yeen K., Bainard, Sean, Shaughnessy, Michael, Bolger, Matt, Koepp, R. Tucker, Salehzadeh, Roya, Mallory, Stephen, Mynderse, James A., Guillen, Pedro, Hernandez, Margarita
View Translation: Geometry-Guided Latent Diffusion for Cross-View Image Synthesis on Ground Vehicles2026-01-75019/22/2026
Synthesizing novel camera views is important for autonomous ground vehicles, with applications in surround-view monitoring, occlusion recovery, and training data augmentation. We present View Translation, a geometry-guided latent diffusion framework that generates a target camera view from a source image, relative camera pose, and an available target-view depth prior. The method combines three components: a Vector Quantized Variational Autoencoder for compact latent encoding, a depth-based warping module that projects the source image into the target view to provide geometric guidance, and a ControlNet-augmented denoising UNet conditioned on source appearance, relative pose, and an auxiliary Image-Depth fusion network. Evaluated on KITTI and a simulated off-road dataset, our method achieves competitive FID while improving LPIPS and PSNR over baseline approaches, supporting cross-view synthesis for ground vehicle perception.
Mayekar, Omkar, Aiyetigbo, Mary, Salvi, Ameya, Samak, Tanmay, Samak, Chinmay, Desjardins, Brendan, Smereka, Jonathon, Brudnak, Mark, Krovi, Venkat, Luo, Feng, Li, Nianyi
The Impact of Observability Constraints on the Planning for Employment of Robotic and Autonomous Systems2026-01-75149/22/2026
Employment of Robotic and Autonomous Systems requires a different paradigm of mission planning. The GTRI Missioneer for Organic Collaborative Kill-Chains (MOCKS) effort was developed to mature the concepts of mission planning in light of autonomy, where the decision making is distributed across the battlespace and each individual entity has limited awareness of global state. The paper presents some initial characterizations of the impact of constraints on periods out of communication, or no-comms windows, on the number of resources required for missions of a certain operational area. This metric, along with the foundational metric of remaining effective range margin are foundational to robust a priori planning.
Spratley, Michael
Scene-Level Multi-View Instance Consistency Using Multi-Feature Vision Fusion for Synthetic Ground Vehicle Environments2026-01-75029/22/2026
Maintaining consistent object identities across multiple camera viewpoints is a critical challenge in synthetic perception environments used for autonomous ground vehicle evaluation. This paper presents a scene-level multi-view instance consistency framework that integrates OpenUSD scene composition, Omniverse Replicator synthetic-data generation, and a multi-feature vision fusion pipeline. The proposed approach combines semantic embeddings from CLIP, patch-level descriptors from DINOv2, geometric correspondences from LoFTR, mask-derived shape invariants using Hu moments, and relative-position priors to associate object instances across views, including visually identical objects. A compact composite scoring function fuses these complementary cues to achieve robust cross-view identity assignment while preserving OpenUSD asset modularity through grouped-prim support. Synthetic experiments across 120 multi-camera scenes demonstrate improved Top-1 Match Accuracy and Identity Consistency Rate, with reduced ID-switch occurrences compared to single-cue baselines. The framework supports scalable, repeatable, and traceable digital engineering workflows for defense-oriented perception evaluation.
Bhattacharya, Sambit, Nakamoto, Kyle
Mobilizing Innovation: Venture Capital Alignment for Defense with an Autonomous Construction Case Study2026-01-75609/22/2026
Defense acquisition often struggles to match the pace of private investment, slowing the transition of mature commercial technologies into military use. This paper examines how aligning government acquisition with venture-oriented business models can increase industry participation, accelerate fielding, and reduce government program office risk. Using autonomous construction as a case study, it highlights how commercial investment has advanced autonomy while traditional procurement limits adoption. The paper outlines approaches such as non-traditional partnerships, phased acquisition, and performance-linked revenue structures to improve flexibility, leverage private capital, and expand the Defense Industrial Base while speeding operational capability delivery. Citation: Mazzara, M., San Nicolas, A., Gadea, J., Himmel, M., Kruger, J., Gill, C., & Simon, A., Soylemezoglu, A., Netchaev, A., Nottage, D., Klein, J. “Mobilizing Innovation: Venture Capital Alignment for Defense with Autonomous Construction Case Study” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA Michigan Chapter, Novi, MI, August 11–13, 2026.
Mazzara, Mark, Nicolas, Austen San, Gadea, James, Himmel, Max, Kruger, John, Gill, Charles “Spuck”, Simon, Andrea, Soylemezoglu, Ahmet, Netchaev, Anton, Nottage, Dustin, Klein, Jordan
Intelligent Optimization Simulation Framework for Test and Evaluation of Autonomous Systems2026-01-75519/22/2026
This paper describes ongoing research and development of an efficient optimization/search–based modeling and simulation framework for rapidly identifying low-performance scenarios in advanced autonomous systems. Ensuring predictable, safe behavior across complex, integrated systems remains a core operational test-and-evaluation challenge. Our goal is to balance rigorous validation with timely deployment. We are developing TEAAS (Test & Evaluation of Advanced Autonomous Systems), a scalable, faster-than-real-time framework designed to uncover critical failure scenarios efficiently. Key features include GPU-accelerated parallel simulation and learning, computational intelligence–based search of optimal parameters, uncertainty quantification for reproducibility, and real-time physics-accurate sensor models. We conducted simulation experiments to evaluate and demonstrate the framework performance for two black-box ground-vehicle autonomous systems. Key results were that adequate uncertainty quantification can be achieved with as few as 10 repeated runs per simulation scenario, sensor realism has a significant effect on failure rate, distinct differences between the two autonomies failure modes were identified, and our efficient optimization/search methods identify critical performance regions in a small fraction of the number of simulations required by a naïve Monte Carlo search.
Snarski, S., Menozzi, A., Persons, B., Lazar, D., Khan, N.
Leveraging Standards-Based MOSA for SWaP-Constrained Deployable Systems with VNX+2026-01-75369/22/2026
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Littlefield, Mark
SOSA Enabled Automotive Technologies Disrupt the Battlefield2026-01-75359/22/2026
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Pilaud, William J.
Adopting Cots Technology for UGV Wheel Drive System2026-01-75619/22/2026
As the defense industry prioritizes speed of play to allow our warfighters to maintain a decisive edge over our adversaries, creativity is needed to leverage COTS effectively. This paper presents a case study of a fast-paced workflow leveraging modeling and simulation, targeted risk testing, thermal characterization, and accelerated life testing. Citation: K. May, J. Costa, J. Boyd, “Adopting COTS Technology for UGV Wheel Drive System,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
May, Ken, Costa, Joao, Boyd, Jake
Stuck-Assist: A Large Multimodal Model-Based Stuck Recovery Assist for Driving on Off-Road Deformable Terrains2026-01-75139/22/2026
Recent advancements in off-road autonomy have shown significant progress in perception, planning, and control frameworks, including end-to-end learning approaches. Comprehensive results have been demonstrated in both simulation and real-world experiments; however, there are significant challenges in critical cases that need further evaluation. One such challenge is the immobilization of autonomous ground vehicles (AGVs) in unstructured off-road environments, which can significantly impact agriculture, space exploration, military operations, and search and rescue missions. Addressing this problem requires recovery strategies that are context-sensitive, adaptable to terrain and vehicle conditions, and effective in integrating multimodal inputs. To this end, this paper investigates the use of a large multimodal model (LMM) providing higher-level planning assistance with human-in-the-loop evaluations for vehicle recovery after immobilization in unstructured off-road terrain. The experimental simulation platform developed was based on the Algoryx (AGX) Dynamics engine for high-fidelity terramechanics interaction and vehicle physics combined with Unreal Engine 5. This platform was further integrated with a driving simulator equipped with steering wheel and pedal interfaces for human-in-the-loop experiments. We evaluated ten representative unstuck scenarios across two deformable terrains (loose sand and compact sand) under two modes: an unskilled baseline, where participants attempted recovery unaided, and a co-intelligence mode, where participants used LMM advisory instructions. The results show that LMM assistance improved stuck recovery rates by 70% compared to unaided and unskilled human driving.
Bhosale, Mayuresh, Whitson, Jordan A., Vahidi, Ardalan, Jia, Yunyi
Terrain-Aware Probabilistic Prediction of Suspension Component Forces in Unmanned Ground Vehicles2026-01-75099/22/2026
Unmanned ground vehicles (UGVs) operating in unstructured environments must account not only for terrain traversability but also for terrain-induced loads that affect component durability. Existing path planning approaches primarily consider obstacle avoidance and mobility, while neglecting cumulative structural degradation due to repeated loading. High-fidelity physics-based simulations can capture these effects but are computationally prohibitive for real-time applications. This study investigates machine learning-based surrogate models for predicting vehicle component reaction forces from terrain height sequences generated using a controlled parametric terrain formulation. Both feed-forward and recurrent neural network architectures are evaluated, and ensemble-based probabilistic techniques are incorporated to quantify predictive uncertainty. Results show that the ensemble long short-term memory (Ens-LSTM) model achieves the lowest prediction error (mean absolute error of 0.621 kN) while maintaining narrow 95% prediction intervals (3.22–4.28 kN). A simpler ensemble feed-forward network (Ens-NN) achieves comparable accuracy (0.626 kN) with reduced model complexity. These results demonstrate that data-driven surrogate models can provide accurate and uncertainty-aware force predictions, enabling the integration of structural reliability considerations into fatigue-aware path planning for UGVs.
Chua, Yang Kang, Mundiwala, Mohammad, Wang, Xudong, Castanier, Matthew, Hu, Zhen, Hu, Chao
Evaluating the Robustness of Segmentation Models against Adversarial Patches in Off-Road Environments2026-01-75209/22/2026
Semantic Segmentation (SS) is critical for autonomous vehicles to navigate off-road environments by identifying drivable terrain. Although models like ResNet34+UNet and EfficientViT have been proposed for these tasks, their susceptibility to localized adversarial patches in unstructured environments remains under-researched. This paper presents a comprehensive robustness evaluation of six real-time SS architectures, including the state-of-the-art YOLOv11 and YOLOv12 segmentation variants against five diverse adversarial patch schemes. Our experiments, conducted on a modified YCOR dataset, demonstrate that EfficientViT is the most resilient architecture, maintaining high accuracy with minimal performance degradation. In contrast, single-stage models like YOLOv11n-seg exhibit significant vulnerability, with pixel accuracy drops reaching 26.55%. We also show how decreases in overall segmentation accuracy impact the segmentation models’ ability to discern traversable terrain from non-traversable terrain.
Salas, Christopher, PesĂ©, Mert D., Li, Bing, Smereka, Jonathon, Cheng, Long
Real-Time Road Intelligence through a Hazard Authentication Model2026-24-00279/21/2026
Advanced Driver Assistance Systems (ADAS) are evolving beyond onboard perception. The ability to dynamically map and share temporary road hazards is important for connected and autonomous driving, but authenticating the data is critical. An in-vehicle hazard recognition layer performs real-time video analysis and geotagging on embedded platforms. Real-time video is analyzed for road hazards—such as potholes, construction zones, waterlogging, fallen trees, roadside accidents, and debris—using onboard cameras and lightweight computer vision models. This metadata is sent to a cloud-based aggregation layer to validate hazard reports. It is of paramount importance to validate hazard reports originating from diverse sources, regardless of their accuracy. This paper presents a mathematical approach to determine an overall Hazard Confidence Score (HCS) based on data received from diverse sources. A unique hazard authentication model is introduced to quantify the credibility of each hazard report using six validation metrics.
Bose, Souvik
Electro-Hydraulic Brake-by-Wire and Off-the-Shelf ABS Integration: Control Design and Hardware in the Loop Validation2026-01-07959/14/2026
This work presents the design of a control logic for an electro-hydraulic brake-by-wire in series with an off-the-shelf ABS unit for motorsport applications. Validation is performed through hardware-in-the-loop testing with a complete hydraulic layout, including the brake-by-wire actuator, the ABS module, and brake calipers. State of the art electro hydraulic brake-by-wire systems are increasingly adopted in top level motorsport and are now transitioning to high performance road vehicles, in combination with ABS and ESC. However, due to motorsport regulations, racing brake-by-wire systems do not incorporate ABS functionality. To combine the performance of motorsport grade actuators with the ease of use required for non professional drivers, a series configuration between brake-by-wire and ABS represents a natural solution. This architecture is also relevant for future road vehicle applications, offering additional redundancy for autonomous driving ready systems. A dedicated hardware-in-the-loop test rig has been developed to perform experimental testing of the complete brake system. Wheel dynamics are simulated in real-time using a single-axle vehicle model, and wheel speed signals are reproduced via a sensor emulator. Preliminary tests show that the original pressure-based brake-by-wire control strategy exhibits poor performance during ABS activation, as ABS operation significantly alters system behavior. To address this issue, an improved control strategy is proposed, introducing a dedicated control mode activated during ABS operation, with a smooth transition back to nominal control once ABS activity ceases. Experimental results demonstrate that the proposed strategy maintains closed-loop stability, avoids excessive pressure oscillations and piston end stop conditions, and, most important, does not interfere with ABS operation. Overall braking performance is fully preserved.
Milivinti, Massimiliano, Gimondi, Alex, Gobbi, Massimiliano, Cantoni, Carlo
Predicting Brake Squeal Occurrence Using Machine Learning with Temporal Feature Analysis of Braking-Torque Test Data2026-01-08129/14/2026
The automotive industry's paradigm shift toward autonomous driving and electrification has introduced new competitors threatening market dominance through differentiated value propositions. In this highly competitive landscape, delivering irreplaceable customer value requires providing sustainable and authentic luxury experiences. Quiet driving represents a tangible value that customers genuinely appreciate. Brake squeal—high-frequency noise arising from friction-induced vibration during braking—negatively impacts customer satisfaction and must be suppressed. Despite significant advances in brake squeal prediction modeling, the irregular nature of squeal generation mechanisms has prevented the development of a generalized predictive model applicable to product development processes. Development and verification remain largely experimental. This limitation constrains early-phase design validation, as brake squeal is highly sensitive to chassis and braking system design. When squeal issues emerge during post-design evaluation, fundamental improvements to pad materials become difficult. Consequently, damping characteristic tuning is employed for mitigation, incurring substantial development costs. This study addresses this challenge through systematic feature engineering of time-series braking data—brake torque, disc rotational speed, disc temperature, and brake pressure—collected during squeal evaluation tests. Based on the hypothesis that environmental conditions and brake system characteristics influence mechanical behavior, time-series features exhibiting strong predictive association with squeal occurrence were derived, and a machine learning model was developed to predict squeal occurrence probability using these features as input variables. The model's predictive performance was validated by comparing squeal probability predictions derived from independent torque performance evaluation data against actual squeal evaluation results. This validation confirms that the model successfully predicts squeal occurrence probability from dynamometer torque performance data alone. Consequently, this approach enables the prediction of squeal occurrence probability in early development phases before formal noise assessment is conducted, streamlining the development process and significantly reducing verification costs while contributing to quieter driving experiences.
Cho, Sunghyun, Yoon, Jungro, Kim, Yoon Cheol, Kim, Jeongkyu, Kim, Sungho, Baek, SongYi, Kim, Won Joon, Choi, Kyung Rok
Optimization of Order Picking in an AMR-Assisted Picker-to-Parts System2026-99-15679/11/2026
With global retail sales expanding and same-day delivery demand on the rise, efficient order picking operations in warehouses have become critical to success. To improve order picking processes, warehouse managers increasingly rely on autonomous mobile robots (AMRs), which improve the performance of traditional picker-to-parts systems. This paper investigates an AMR-assisted picker-to-parts system in which a set of customer orders must be fulfilled. The orders are first batched, and the resulting batches are assigned to individual pickers. Each picker works in a batch-by-batch manner, manually retrieving items from picking aisles and handing over the completed batch to an AMR waiting at the cross aisle. After receiving a full batch, the AMR transports it to the designated depot before returning to serve the next batch. The objective is the minimization of the total tardiness of all orders. The problem is formulated as a mixed-integer programming (MIP) model, and several effective heuristic algorithms are developed. Extensive computational experiments are conducted to evaluate the performance of the proposed algorithms and compare them with a commercial MIP solver.
Jin, Bo, Peng, Jianxin
Adaptive MPC Path Tracking Control for Autonomous Vehicles2026-99-15709/11/2026
This work introduces a Model Predictive Control (MPC) path tracking controller, which is developed to boost robustness, tracking precision, and vehicle stability when navigating high-speed and high-curvature driving scenarios. First, a 3-degree-of-freedom (3-DOF) dynamic model of the vehicle is established to serve as a reference. Next, a linear time-varying MPC control algorithm is formulated, with constraints on yaw rate, lateral velocity, and road boundary conditions taken into account; a comprehensive performance metric that balances tracking accuracy and control smoothness is also defined. Third, the time-domain parameters of the MPC framework are optimized using an improved genetic algorithm. Finally, the effectiveness and accuracy of the proposed controller are validated via co-simulation experiments conducted on the Matlab/Simulink and Carsim platforms. Simulation results demonstrate that the controller exhibits excellent robustness: the peak lateral tracking error is only 0.05 m on high-friction roads and 0.12 m on low-friction roads, with a maximum heading error of 0.15°. Additionally, the vehicle’s dynamic stability is notably enhanced: the yaw rate is reduced by 9.6% and 15.7% on high- and low-adhesion roads, respectively, while the sideslip angle is decreased by 13.2% and 18.4% under the same conditions.
Yu, Hanzhengnan, Hou, Xiaoyi, Zhang, Hao, Zhou, Weichen, Liu, Yu
Affine Formation Control of Multi-Agent Systems with Switching Topologies2026-99-11289/11/2026
Tackling the heavy computation of affine formation control under switching topologies—rooted in frequent stress matrix recalculation—this paper presents a distributed control framework fusing consistency estimation with dynamic error constraints for efficient coordination. In a leader-follower architecture, affine transformation parameters are estimated by followers using local information—global stress matrix solutions are thus avoided. A time-varying constraint function and Lyapunov stability analysis are devised to ensure tracking errors converge to specified accuracy within a predetermined time. Both theoretical analysis and simulation results show that this method greatly simplifies computation. It also supports flexible formation transformations such as translation and scaling, making it a stable and reliable solution for dynamic scenes.
Liu, Guicai, Li, Jianzhen, Zhou, Junyi, Tang, Jiye
A Spatio-Temporal Graph Network with Informer-TCN Fusion for Vehicle Trajectory Prediction on Highways2026-99-11049/11/2026
Accurate vehicle trajectory prediction is essential for the driving safety and efficiency of autonomous vehicles. However, this task remains challenging due to the complex spatial interactions among traffic participants and the wide range of temporal dependencies in motion sequences. To address these issues, this paper proposes a novel hybrid deep learning framework suitable for cloud-based control platforms, providing a foundational algorithmic solution for vehicle-infrastructure cooperative perception and decision-making. The proposed architecture employs an Adaptive Graph Convolutional Network (AGCN) to adaptively learn spatial relationships and interactions among vehicles. It also utilizes the Informer model, known for its efficient ProbSparse self-attention mechanism, to capture long-term temporal dependencies in trajectory sequences. Furthermore, a Temporal Convolutional Network (TCN) is integrated to enhance the model’s ability to learn fine-grained local temporal features. The proposed model is evaluated on the NGSIM dataset. The dataset is chronologically ordered and split into training (80%), validation (10%), and testing (10%) sets. Experimental results show that the proposed method achieves an average minADE of 2.032 m and minFDE of 2.866 m over a 5-second prediction horizon, outperforming several baseline models such as LSTM, CNN-LSTM, and Social-GAN. These results indicate the effectiveness of the AGCN-Informer-TCN combination for trajectory prediction. The study suggests potential for integration into intelligent transportation cloud control platforms.
Liang, Ziyan, Yuan, Rui, Zhou, Pengying, Yang, Shu, Li, Weidong, Zhang, Zijian
Research on an Evaluation Method and Application of Autonomous Vehicles in Traffic Flow Environment2026-99-15629/11/2026
Traffic flow environment testing is indispensable in the research and evaluation process of intelligent vehicles. However, the current vehicle evaluation systems mostly focus on simple dynamic scenarios and lack a comprehensive assessment of vehicle performance in complex traffic flow environments. In view of this, this paper constructs a multi-dimensional comprehensive performance evaluation system for vehicles in traffic flow environments. Firstly, based on the randomness and dynamics of traffic flow, four core evaluation dimensions covering safety, comfort, efficiency, and economy are constructed. In terms of security, the collision time and Predicted Safety Metrics are adopted. This enables the dual quantification of immediate collision risks and dynamic obstacle avoidance capabilities. The comfort evaluation focuses on the impact of vibration frequency on the human body and constructs a graded quantitative index. Efficiency and economy evaluation models are built through time cost and energy consumption cost. The verification is carried out under different traffic flow scenarios, and the final results demonstrate the consistency between the evaluation method of this paper and the expert evaluation method, thereby verifying the rationality of the evaluation system presented in this paper.
Wang, Guangyu, Song, Shiping, Zhang, Cheng, Qu, Ge, Yu, Xiaojun
Research on High-Reliability Testing Systems for Intelligent and Connected Vehicle2026-99-11119/11/2026
With the large-scale application of intelligent connected vehicles, the verification of their functional safety and reliability has become a core bottleneck in the industrial development. The traditional real- vehicle road test method can no longer meet the current demand for large-scale test verification due to problems such as high cost, low efficiency, and difficulty in reproducing dangerous scenarios. This paper studies the vehicle-in-the-loop simulation test system based on a digital twin. By constructing a virtual scenario highly consistent with the real world, physical-level multi-source perception signals are simulated and mapped to the system under test to enable high- reliability verification of real vehicles. In terms of lateral and longitudinal control functions, multiple sets of test cases are selected respectively for comparison between road tests and virtual simulation tests. The results show that the accuracy of key indicators is above 90%, which provides practical reference for the subsequent test and verification system of high-level autonomous driving.
Hou, Quanshan, Tang, Ke, Gao, Tian, Chen, Tao, Zhou, Si
Emotional Impacts of Manual and Assisted Driving Modes under Varying Time Pressures: A Scenario-based Simulation Study2026-99-15089/11/2026
Emotion as a critical psychological variable in driving behavior has been extensively confirmed to exert a profound influence on traffic safety. With the advancement of autonomous driving technologies, the application value of emotion regulation mechanisms in intelligent transportation systems is gaining importance, given their proven impact on reducing unsafe driving tendencies and improving human-machine interaction quality. However, systematic research on the mechanisms underlying emotional variation across different driving modes remains relatively scarce. This study developed a questionnaire based on the Chinese version of the Driving Anger Scale (DAS), incorporating 19 traffic scenarios and corresponding video stimuli, to examine the impact of manual and assisted driving on driving anger under varying levels of time pressure. In addition, the moderating effects of individual factors such as age and education were analyzed. Descriptive statistics, independent samples t-tests, and one-way ANOVA were applied to 113 valid responses to construct a three-dimensional analytical framework involving driving mode, time pressure, and demographic variables. The results indicate that under high time pressure, assisted driving systems significantly reduce drivers' anger levels in typical delay-related scenarios compared with manual driving. Age is identified as a key moderating factor influencing emotional responses. This research provides theoretical support for the development of emotion-aware driving intervention systems and offers empirical evidence for the formulation of future strategies for driving emotion regulation.
Zhang, Tingjia, Li, Ruiheng, Zhu, Tong
A Vision-Based Vehicle Collision Warning Algorithm Integrating YOLOv8 and DeepSORT2026-99-11079/11/2026
With the continuous development of autonomous driving technology, vehicle collision warning systems are playing an increasingly important role in this field, promoting the progress and improvement of the whole autonomous driving field. But existing methods have low detection rates and unstable multi-target tracking performance. In particular, the estimation of relative motion parameters is still inaccurate due to the loss of direction information when relative speed is described as a scalar quantity. These limits produce easy collisions in the judgment of the car in a dangerous traffic environment. To solve these problems, a vehicle collision warning algorithm based on YOLOv8 and DeepSORT is proposed in this paper. YOLOv8 is introduced to detect the vehicle precisely, and DeepSORT is used to enhance multi-target vehicle tracking. The geometric principles of monocular vision are applied to extract key motion parameters such as distance and direction-signed relative speed. A classification logic is designed to distinguish between positive and negative relative velocities, enabling more accurate judgment of collision risk levels. In order to further enhance the reliability of the system, a four-stage cascaded false-alarm suppression mechanism is proposed. By adding velocity direction validation, distance validity checks, adaptive confidence thresholds, and a temporal consistency verification mechanism, the false alarm rate is reduced, and the proposed approach can realize direction- aware velocity estimation without requiring additional sensors and can be easily integrated into the existing YOLOv8 perception system.
Qin, Xiaoyu, Li, Wei
3D Dynamic Object Detection with Multi-Modal Temporal Fusion2026-99-11179/11/2026
This research aims to address the critical challenge of accurately detecting and estimating the state of dynamic objects in autonomous driving. Traditional 3D object detection methods often struggle with motion perception, particularly in velocity estimation, due to the lack of information in single frame perception. We propose a novel framework that enhances the BEV representation with temporal modeling. The core of our method is a two-stage temporal fusion process. First, we align historical BEV features to the current coordinate frame to eliminate the interference of ego-motion. Subsequently, a dedicated temporal fusion encoder, architected with residual connections and a Feature Pyramid Network, refines the aligned multi-frame BEV features to capture complex motion patterns and improve multi-scale object representation. This approach directly tackles the problem of motion decoupling. By aligning features, we disentangle object motion from ego-motion. The temporal fusion encoder then mitigates the positional ambiguity of moving objects in the fused BEV space, a common issue in simple feature concatenation, leading to more robust detection. We built a dataset following the structure of the nuScenes dataset, using data collected from an autonomous driving simulation platform. The evaluation results on our simulation dataset demonstrate that the proposed temporal module achieves a 13.0% improvement in NDS score and a substantial 29.7% reduction in velocity error (mAVE). These results demonstrate that our temporal fusion strategy effectively enhances 3D detection accuracy in dynamic scenarios.
Shao, Mengjia, Li, Wei, Bai, Jie, Zhu, Shaoxiong, Xu, Chenjie
Analysis of Latency-Influencing Factors in Vehicle-Road-Cloud Integrated Systems Based on Fault Tree Analysis and Analytic Hierarchy Process2026-99-15439/11/2026
Vehicle–road–cloud integrated systems have great potential in terms of improving traffic efficiency and achieving intelligent automatic driving through the integration of on–board terminals, roadside facilities, and cloud computing. However, their operational capabilities are heavily reliant on ultra-low-latency collaborative communication. This paper constructs a latency fault tree model to comprehensively analyze multi-source triggering paths of computation delay and reveals the formation mechanism of the delay path from “germination–induction–evolution”. On this basis, the Analytic Hierarchy Process is used to construct a three-level evaluation framework, and the influencing factors are quantitatively evaluated using NS-3 simulation data of the 004-V2X Communication Performance Testing Dataset. The result shows that the weight value of the network communication layer is the largest, 0.498, which shows that the bottleneck of performance in network communication is the wireless link quality. The cloud processing layer is second 0.327, which is dominated by the computational complexity and resource allocation policy. The impact of the onboard terminal layer is the smallest, 0.175. The FTA–AHP framework supported by empirical data can find the key factors affecting delay, which can help engineering optimization. It is noted that the AHP consistency check (CR) just checks the inner transitivity of expert judgment (i.e., the matrix consistency), while it cannot assure the objectivity and the bias elimination. We reduce the subjectivity by combining multiple experts, anchoring judgment with the simulation data, and performing a sensitivity check on the perturbation of the weights.
Xu, Yunchuan, Wang, Xiaomeng, Wang, Yan
SAE TOMORROW TODAY - Liability in the Age of Autonomous Vehicles135839/8/2026
When AI is driving, who's responsible? Listen in as we sit down with Jennifer Dukarski, Emerging Tech, Media, and Intellectual Property Shareholder at Butzel, to unpack the rapidly evolving legal landscape surrounding SDVs and AVs. From AI decision-making and over-the-air updates to data retention, cybersecurity, consumer education, and the growing complexity of liability, we explore how existing legal frameworks are being tested by next-gen mobility. Whether you're developing autonomous technology, managing risk, or shaping industry standards, this conversation offers an insightful look at the legal challenges that will define the future of transportation. Have your own thoughts on this topic? 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 your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Resilient Trajectory Tracking Static Output-Feedback Control for Automated Vehicles under Denial-of-Service Attacks10-11-01-00028/24/2026
The safety and reliability of autonomous vehicles are critically linked to network communication quality. Consequently, threats such as Denial-of-Service attacks pose significant risks by disrupting communication between essential components and jeopardizing driving safety. This article presents a robust trajectory tracking control method resilient to such attacks. Utilizing a Linear Parameter–Varying control framework, the method effectively addresses time-varying vehicle speeds and incorporates a norm-bounded strategy to manage tire behavior uncertainties. By employing a Static Output-Feedback scheme, it avoids the need of costly sensors while maintaining a straightforward control structure suitable for real-time implementation. Using Lyapunov’s method, we analyze the system stability under attack conditions, ensuring the controller remains robust against disturbances. The design of the resilient controller is formulated as a convex optimization problem with Linear Matrix Inequalities constraints. The effectiveness of the proposed controller is validated through co-simulation in MATLAB/CarSim®, where it outperforms several state-of-the-art controllers across different driving scenarios, maintaining consistent tracking performance despite varying attack severity levels.
MelĂ©ndez-Useros, Miguel, Viadero-Monasterio, Fernando, Nguyen, Anh-Tu, LĂłpez-Boada, MarĂ­a JesĂşs
SAE TOMORROW TODAY UNPLUGGED - The Power Struggle Shaping Autonomous Trucking135818/14/2026
With too many cooks in the kitchen, can innovation ever reach the road? In this Unplugged episode of SAE Tomorrow Today, host Grayson Brulte uses the culinary world as a powerful metaphor to explore one of the autonomous trucking industry's most pressing issues: who gets "veto power"? As startups challenge established OEMs and suppliers, questions around control, competition, and liability are shaping the future of commercial autonomy. Are legacy players protecting safety or their market position? And when disruptive newcomers refuse to back down, who ultimately determines the recipe for success? Tune in for a fresh perspective on the power struggles, partnerships, and industry dynamics that could define the next chapter of autonomous trucking. Have your own thoughts on this topic? 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 your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Attention and Intrinsic Curiosity-Enhanced Deep Reinforcement Learning for Path Planning in Dynamic Environments2026-01-50567/31/2026
Efficient and reliable path planning remains a core challenge for autonomous vehicles operating in dynamic and crowded environments. Although Deep Reinforcement Learning (DRL) has shown considerable potential in autonomous decision-making, it still faces challenges such as insufficient feature extraction, sparse rewards, and low obstacle avoidance efficiency in complex scenarios. To address these issues, this paper proposes an end-to-end path planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization (PPO) algorithm, the framework incorporates a dual-channel attention convolutional neural network module (Attention-CNN) to enhance spatial and semantic understanding of dynamic obstacles, and introduces an Intrinsic Curiosity Module (ICM) to promote active exploration in sparse-reward settings. Furthermore, a reactive avoidance reward function based on velocity-obstacle theory is designed and embedded to achieve real-time proactive collision avoidance in highly dynamic environments. Experiments are conducted in a semi-structured dynamic crowd scenario constructed on the GAZEBO simulation platform. The results demonstrate that PPO-ICM-Attn achieves significant improvements in key metrics such as path success rate, travel time, and path efficiency compared to baseline methods like A*+DWA and standard DRL. Although the gap remains in path efficiency compared to A*+DWA, the proposed method exhibits superior robustness and navigation performance overall, validating its effectiveness in complex dynamic environments.
Shen, Shiquan, Liu, Jiahao, Chen, Zheng, Li, Zongdian, Zhao, Yuting, Wu, Minggong, Zhao, Jie, Qin, Zongquan, Wang, Yanfeng
Research on Fault-Tolerant Control Strategy for Drive System Failure of Corner Module-by-Wire Chassis Vehicles2026-01-50547/31/2026
As an emerging research focus, corner module-by-wire chassis vehicles overcome the limitations of traditional chassis in flexibility, cost, and development efficiency, serving as a key infrastructure in the autonomous driving era. However, their numerous actuators raise significant actuator failure risks. This paper analyzes the characteristics of such vehicles and studies fault-tolerant control for drive system failures. Firstly, a vehicle model for the corner module-by-wire chassis was established based on CarSim and Simulink. Then, a hierarchical lateral stability control strategy was designed for the non-faulty actuators: the decision control layer employed sliding mode control (SMC) and fuzzy PID control, selecting the optimal method to output additional yaw moments; the control allocation layer distributed the upper-level target yaw moments based on the vertical load of the tires, converting them into individual wheel torques to meet the constraints. For the drive system, potential fault scenarios were analyzed and their fault modes were classified. By using the non-faulty actuators for torque reconstruction, fault-tolerant strategies were designed for single-motor, diagonal dual-motor, and coaxial dual-motor faults. A co-simulation platform was built using MATLAB/Simulink and CarSim, testing the stability control strategies under three fault modes in constant-speed straight-line and double-lane change conditions. Simulation results show that the designed drive system fault-tolerant control strategy effectively maintains the vehicle’s expected dynamic performance and stability.
Zheng, Hongyu, Zhang, Tianhao, Zhang, Yuzhou
Development and Technical Verification of A Lunar Dust Environment Simulation Mechanism2026-99-01757/31/2026
Lunar dust consists of extremely fine particles and exhibits electrostatic charging properties and electrostatic adhesion. These characteristics cause lunar dust to be highly susceptible to mobilization during lander touchdowns, rover traversals, and human activities, forming widely distributed dust clouds. Lunar dust contamination not only abrades spacecraft and equipment to impair their performance but also poses a threat to astronauts’ safety. To verify the impact of the lunar dust environment on exploration equipment components, a simulation mechanism adaptable to the thermal vacuum test environment was designed. This mechanism is integrated into the lunar environment simulation system and uses a vacuum stepper motor to drive a ratchet mechanism, enabling precise vibrational injection of simulated lunar dust. It mainly consists of a pretreatment mechanism, a particle sedimentation mechanism, a shielding mechanism, and an ultraviolet (UV) irradiation system. Considering the vacuum operating environment, alternating high and low temperature conditions, as well as the strict requirements for the mechanism’s compact size and high reliability, this paper analyzes in detail a series of problems encountered during the development of the mechanism and their corresponding solutions. Stainless steel and polytetrafluoroethylene (PTFE) were selected as the main materials for the mechanism. Meanwhile, active temperature control measures were adopted to actively regulate the temperature of components such as the motor. Ultimately, the mechanism can withstand alternating high and low temperatures ranging from -150°C to 150°C and a vacuum environment of 5 × 10^–6 Pa. Under this environment, the mechanism can achieve vibration frequency adjustment within the range of 1-5 Hz, and realize the sedimentation of simulated lunar dust particles with a particle size of less than 200 μm over an area of 150 mm × 150 mm. After sedimentation, the simulated lunar dust particles can be charged through the photoelectric effect.
Xu, Menglong, Lv, Shizeng, Li, Guohua, Gong, Jie
Impacts of Automation and Electrification Trends on Pneumatic Braking Systems of Commercial Vehicles2026-36-03417/30/2026
Historically, the demand for advanced technology, efficiency, and safety has been a primary driving force in the evolution of commercial vehicles, particularly with respect to braking systems. More recently, the increasing levels of vehicle autonomy and electrification have emerged as irreversible trends, significantly accelerating the development of new functionalities and innovative electrical/electronic [E/E] architectures. These advancements are essentially focused on performance optimization, risk mitigation, and enhanced system reliability through the application of functional safety and cybersecurity standards, thereby shaping the current landscape of braking system design. From an efficiency standpoint, braking systems with higher levels of electronic content, functional integration – included with regenerative braking systems - and harmonization have been developed to improve energy efficiency and support global scalability. Concurrently, new system configurations are continuously being introduced to enhance vehicle safety and advanced driver assistance capabilities, in alignment with evolving regulatory requirements and market expectations. This paper evaluates the impacts of automation and electrification on commercial vehicle pneumatic braking systems, focusing on Anti-lock Braking Systems [ABS], Electronic Braking Systems [EBS] and air management platforms. It provides a technical overview of both architectures, assessing their capabilities to meet modern requirements such as integration with advanced vehicle architecture, regenerative braking for electrified applications, and Advanced Driver-Assistance Systems [ADAS] support. The study details the evolution of air management systems, with emphasis on electrified vehicles, including key functions such as air compressor charge control, Air Processing Unit [APU] desiccant regeneration, and electronic control strategies. Additionally, it examines key drivers of braking system evolution, braking system selection considering ADAS regulatory developments, Net Zero strategies, and automation trends. The paper further evaluates compliance with functional safety and cybersecurity standards and assesses the readiness of both platforms for emerging mobility concepts. Finally, it highlights the risks of deploying higher levels of autonomy in heavy-duty towing vehicles when operating with non- ABS semi-trailers, identifying this as a critical area for further investigation.
Guarenghi, VinĂ­cius Mendes, Nicora, Fabio, Pizzi, Rafael Fortuna, Resende, Angelo Roberto Rodrigues, Pinto, Gustavo Laranjeira
A Review of Physical and Digital Infrastructure Standards and Guidelines to Support Deployment of Automated Vehicles in Rural Environments12-09-04-00307/23/2026
Connected and Automated Vehicles (CAVs) represent a transformative innovation poised to revolutionize roadway transportation by leveraging automated driving systems equipped with advanced sensors, high-performance computing, and communication technologies. While urban areas are the primary focus of current CAV developments, rural transportation systems risk being left behind despite the significant benefits that CAVs can bring to these regions. This article, therefore, explores the physical and digital infrastructure requirements for the safe deployment of CAVs in rural areas, drawing insights from standards, recommendations, and guidelines developed by leading standard organizations. The study highlights the specific design of physical infrastructure, including traffic signs, traffic signals, and pavement markings, and digital infrastructure, including communication, sensing, and mapping, to ensure rural communities are effectively prepared to benefit from the potential of CAVs. As its primary contribution, this article provides a comprehensive review of existing standards and guidelines relevant to rural CAV deployment. By synthesizing guidance across multiple standard-setting organizations, this review delivers a structured analytical assessment of existing standards, revealing their limitations and misalignment with rural transportation contexts while highlighting emerging good practices. The article clarifies the applicability of current guidance to rural infrastructure, identifies systemic infrastructure-related failure modes, and informs context-aware planning considerations for efficient and scalable CAV deployment in rural areas.
Zakaria, Mohammed, Getahun, Tesfamichael, Tavasoli, Mahsa, Pandey, Venktesh, Sarrafzadeh, Abdolhossein, Karimoddini, Ali
Coupled Lateral–Longitudinal Trajectory Tracking Control for Autonomous Vehicles Based on Multi-Agent Reinforcement Learning12-09-03-00247/23/2026
Trajectory tracking control serves as the core operational component of autonomous vehicles, directly determining driving safety and passenger comfort by ensuring control precision and stability. To enhance the tracking accuracy and stability for autonomous vehicles, this study proposes a coupled lateral–longitudinal trajectory tracking controller based on multi-agent reinforcement learning. The framework first establishes a Model predictive controller (MPC) derived from vehicle dynamics, formulating the lateral control process as a Markov decision process. A reward function incorporating lateral error, heading error, and steering angle is designed, followed by the construction of a Deep Q-Network (DQN) Agent to optimize the prediction horizon of the MPC. Subsequently, a position–velocity dual-loop PID controller is developed for longitudinal control, with its parameter optimization strategy learned through a Deep Deterministic Policy Gradient (DDPG) Agent. The Extended State Observer (ESO) is incorporated to perform steering angle compensation for internal modeling errors and external disturbances. Co-simulation experiments are conducted in CarSim and MATLAB/Simulink, and the results demonstrate that the coupled controller achieves superior tracking accuracy and stability in both overtaking and lane-changing scenarios compared with the decoupled controller.
Kun, Feng, Jinxiang, Zhai, Li, Wenli
North American CAV Performance Data StandardWP-00157/22/2026
As the deployment of connected and automated vehicles (CAVs) expands, the need for a consistent, cross-industry approach to performance relevant CAV data exchange is becoming more pressing. Vehicle developers, infrastructure owners and operators (IOOs), and technology providers generate and consume data that support safety, mobility, and operational efficiency, yet much of the data remains fragmented, inconsistently formatted, and difficult to reuse across systems. To address these gaps, the Society of Automotive Engineers (SAE) and the Canadian Standards Association (CSA) convened a multi-stakeholder workshop on November 3, 2025, with participants representing original equipment manufacturers (OEMs), automated driving system (ADS) developers, state and local agencies, standards bodies, and technology partners. The workshop focused on identifying challenges, clarifying needs, and outlining a path toward a North American CAV Performance Data Standard. Key themes from the workshop included: -The need for a shared data language to support safe and interoperable CAV operations. -The lack of consistent formatting, labeling and visibility regarding who produces and consumes data. -A “start small, iterate and scale” approach beginning with well-defined use cases. -The need for technical harmonization and governance structures that build trust and support sustained coordination. This white paper summarizes the key findings and outlines a practical approach to developing a Version 0.1 base-layer data standard that can support measurable progress in 2026 and beyond.
Nesheli, Mahmood
Asteroid Landing Navigation Based on Invariant Extended Kalman Filter2026-99-18567/17/2026
Optical navigation serves as a critical modality for autonomous guidance during small celestial body landing missions. To address the inherent strong nonlinearities in both the lander’s dynamic model and optical observation model, this paper investigates an invariant extended Kalman filter algorithm based on Lie group structures. First, we establish the state model and optical observation model on the special Euclidean group. Subsequently, a linearized right-invariant error dynamics equation is derived using invariance theory, along with the formulation of state prediction models. Furthermore, the feature vector observation model is modified into a right-invariant observation form, enabling state correction through exponential mapping of innovation vectors. Numerical simulations using asteroid Eros 433 demonstrate that the proposed invariant extended Kalman filter (InEKF) outperforms the conventional extended Kalman filter (EKF) in both estimation accuracy and convergence speed. Notably, the algorithm eliminates the need for online Jacobian matrix computations, satisfying the stringent navigation requirements for autonomous landing operations. The results validate the effectiveness of Lie group-based filtering in handling the nonlinear geometry of pose estimation for irregular celestial bodies.
Liu, Zhengdong, ZHU, Shengying
Multi-Frame Centroid Extraction Method for Optical Navigation During the Approach Phase of Small Body2026-99-18517/17/2026
Autonomous optical navigation is one of the important navigation methods for the small bodies approach phase. To improve optical navigation performance during the approach phase to a small body, this paper presents a method for extracting the target centroid from sequential optical images. The process begins with fitting a minimum enclosing ellipse to the detected contours in each frame to obtain an initial estimate of the centroid. Building upon this, edge corner points across adjacent images are matched using normalized cross-correlation, and their displacement is tracked using optical flow techniques. The observed pixel trajectories are analyzed, and a predictive model of pixel motion is formulated based on the geometric relationship between the detector and the small body. By combining the directly extracted centroids with the predicted motion of key pixels, a fusion strategy is developed to improve the reliability of the centroid estimation. Finally, numerical simulation results demonstrate that the method significantly improves the accuracy of centroid extraction, thereby enhancing the overall performance of optical navigation during approach operations.
Liu, Jing, Zhu, Shengying
Landing Site Selection and Visual Scene Generation Based on LiDAR Point Cloud2026-99-18197/17/2026
This paper focuses on autonomous drone landing scenarios. Addressing the core requirements of accurate landing site assessment and intuitive visual presentation, it conducts in-depth research on the application of 3D LiDAR (TOF technology) point cloud data. LiDAR captures point cloud data containing 3D coordinates and reflection intensity values. While sparse, non-uniform, and disordered, its high measurement accuracy and strong anti-interference capabilities make it a key sensor for landing terrain perception. Based on a review of recent research results from related teams, this study designed and implemented a comprehensive technical solution: First, raw point cloud data is acquired via the UDP protocol combined with an SDK interface. Preprocessing is then performed using voxel grid filtering (downsampling) and radius filtering (denoising). The assessment area is then divided into a row-by-column grid. A sliding window method is used to calculate the elevation difference, empty grid ratio, flatness, and slope of each grid. Based on these attributes, the grids are classified into six categories: Risk, Warning, Blank, Unknown, No Landing, and Landing. Finally, a grid attribute coloring method and OpenGL 3D rendering are used to generate the visual scene. Through the development of verification programs and moving obstacle experiments, it has been proven that the solution can efficiently process point cloud data and accurately identify safe landing areas, providing key technical support for the engineering realization of the autonomous landing function of drones, and also laying the foundation for the intelligent development of drone landing decisions in complex environments.
Guo, Hangyu, Shi, Zhe
A Method for On-Orbit Testing of Long-Term Docking of Manned Spacecraft at the Space Station2026-99-18487/17/2026
To ensure the successful implementation of the separation, evacuation, and return processes of manned spacecraft after long-term docking at the space station, regular on-orbit health assessments must be conducted. Based on this requirement, a technical method for evaluation through autonomous on-orbit testing is proposed. First, the docking status and characteristics of the manned spacecraft’s systems, such as information management, crew environmental control, thermal control, power management, docking function, attitude, and orbit control function, are described. Then, the functional requirements for the separation, evacuation, and return of the manned spacecraft, such as the relative measurement, the relay communication, TT&C and data transmission, image and voice, instrument display and alarm, and the attitude measurement, are analyzed. Subsequently, the on-orbit testing system, test items, test procedures, and test methods for health assessment are detailed. It also provides the design of TT&C support, the design of energy support, and the main principle explanation for autonomous on-orbit testing of the system.
Cheng, Wei, Nan, Hongtao, Tian, Ye, Zhao, Zheng
Research on UAV Anti-UAV Strategies Based on Deep Reinforcement Learning2026-99-18157/17/2026
This paper constructs a reinforcement learning framework based on the PPO algorithm for drone air combat to solve 1v1 pursuit-evasion in 2D beyond-visual-range air combat. Firstly, the mission scenario is modeled, defining key roles of ATA and AA. Then, state transition models of pursuer and evader are built based on flight kinematics. To handle reward sparsity in policy network training, a dense reward function combining distance and angle rewards is designed to guide the agent in learning tail-chasing and interception strategies. Using the Actor-Critic architecture, deep neural networks implement the decision-making and evaluation modules. The PPO algorithm trains the pursuing drone in a simulation. Results show that after ~5 million steps, the agent learns a stable strategy, completing tasks promptly and generalizing well in unseen scenarios. This research offers ideas for drone combat and guidance, and supports autonomous decision-making in complex air battles.
Yu, Kangjie, Gong, Zheng, Hu, Runchang, Liu, Huixiang
An Exploration of the Potential of Urban Linear Space under the Background of Fully Autonomous Vehicle System in Beijing2026-99-05517/10/2026
The technology of autonomous vehicles has become the bellwether for the next transportation evolution. Based on the system of level 5 autonomous vehicles (fully autonomous vehicles), there will be space released from the existing urban context, including linear space, nodular space, and intersected space because of the enhancement of transportation efficiency and organization. The study took Beijing as an example to explore the linear space releasing potential under fully Autonomous Vehicles system to provide a reference for future urban planning. Considering saturation flow rate, speed, parallel throughput, vehicle occupancy, and safe headway, we quantitatively analyzed the potential release from various types of urban roads. The results shows that the expressways, arterial roads, secondary arterial roads, and branch roads could release up to 50%, 66% 50%, and 75% of the road space, respectively. The study verified that fully AV system can release great amount of public space, and provided primary modes for the reformation of urban contexts in the future.
Ding, Yufei, Hou, Shuyu
SAE TOMORROW TODAY - Building a Robust Engineering Culture in Our Nation's Capital135767/10/2026
How does a strong engineering community help shape the future of mobility? Join us as we sit down with Brent Yacobucci, Acting Director of The John W. Kluge Center at the Library of Congress, and Chair of the SAE Washington DC Section, to discuss the intersection of engineering, technology, and public policy in driving innovation in our nation's capital. From vehicle safety regulations and AI to autonomous vehicles, Brent explains why today's engineers must understand not only emerging technologies but also the policies that influence how they're developed, deployed, and adopted. The conversation also explores how the SAE Washington DC Section brings together industry, government, academia, and students to foster collaboration, innovation, and the next generation of engineering leaders. If you're in the Washington, DC area, join us on September 28 for the SAE Washington DC Section Charity Golf Outing to support student STEM programs and connect with fellow engineering professionals. We'd love to hear from you! Share your comments, questions or ideas for future topics with Grayson on Twitter or send them to podcast@sae.org. Follow SAE on LinkedIn, Instagram, Facebook, Twitter, and YouTube.
Patterson, Lori
Path Planning Algorithm for Self-Driving Cars Based on High-Precision Maps and Improved A*2026-99-05887/10/2026
To address the limitations of the traditional A* algorithm in lane-level navigation, we propose an autonomous vehicle path planning algorithm based on high-precision maps and an improved A* algorithm to ensure effective application in complex traffic environments. We construct a hierarchical high-precision map based on the Lanelet2 framework to achieve structured modeling of complex road environments. To address the adaptability issues of the A* algorithm in lane-level navigation, we propose optimization schemes, including heuristic function improvements, path segment division, and target point validity verification, to ensure that vehicles can autonomously change lanes on multi-lane roads. By combining dynamic programming (DP) and quadratic programming (QP), we ensure the safety and smoothness of the path. Simulation results demonstrate that the optimized algorithm enables smooth stopping and starting at traffic lights in structured road environments and autonomous lane changes on multi-lane roads. Compared to using DP alone, QP provides smoother and safer driving paths and exhibits superior obstacle avoidance performance in speed planning. This method effectively ensures the rationality of path planning in complex road environments while strictly adhering to traffic rules, thereby enhancing the safety and reliability of path planning.
Wang, Siyu, Zhou, Rong, Shi, Tian, Xu, Zhen, Zhao, Zhiguo
Optimizing Autonomous Obstacle Avoidance with PON Architecture and SDN-Based Dynamic Scheduling2026-99-05927/10/2026
This study addresses the challenges of communication delays and system stability in autonomous obstacle avoidance (AOA) systems under next-generation vehicular electronic/electrical architectures. A centralized PON-based architecture is proposed, leveraging XGSPON technology to enhance bandwidth capacity and reduce electromagnetic interference, while rigorously analyzing worst-case in-vehicle communication (IVOC) delays. To mitigate latency impacts, a Software-Defined Networking (SDN)-driven dynamic scheduling strategy prioritizes safety-critical data streams (e.g., environmental perception, motion control) through adaptive resource allocation. Further integrated with a robust H-infinity LQR controller, the co-design framework ensures precise trajectory tracking and suppresses steering oscillations under communication uncertainties. Simulation tests validate the framework's efficacy, demonstrating significant reductions in loop delays and improved dynamic stability in complex scenarios. This work bridges communication efficiency and control robustness, offering a scalable solution for advancing safety-critical autonomous driving systems.
Wang, Wenwei, Han, Muchen, Cao, Wanke
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