Browse Topic: Autonomous vehicles

Items (3,041)

An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study

2026-01-0097To 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 feedforward compensation-based trajectory tracking control

2026-01-0028To be published on 04/07/2026

Advanced autonomous driving is a critical component in the intelligent development of new-generation electric vehicles. Research on reliable chassis control algorithms ensures the safety and stability of autonomous vehicles during operation. To enhance the control performance of autonomous vehicles and improve the accuracy of trajectory tracking, this paper proposes a data-driven feedforward compensation trajectory tracking control approach. By optimizing the design of the feedforward compensation loop, systematic errors and latency in the vehicle’s steering system are mitigated, thereby enhancing the precision and robustness of the control algorithm. Initially, the paper analyzes the control errors present when the vehicle responds to controller commands. Subsequently, the paper focuses on the steering angle errors in trajectory tracking, identifying and analyzing the most relevant factors. A time-delay neural network (TDNN) based on data-driven principles is designed to model and

Yang, Yijin, Yuan, Yin, Wang, Zhenfeng, Su, Ailin, Zhang, Zhijie, Lu, Yukun

High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety

2026-01-0034To be published on 04/07/2026

Ensuring the safety of vulnerable road users is a critical challenge in the development of advanced autonomous driving systems. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and manageable. Compared to pedestrians, vehicles often travel at significantly higher relative speeds when interacting with bicyclists, yet bicyclist motion is generally more predictable and governed by clear traffic rules, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. By using this approach, CLFs enforce system stability, ensuring the vehicle follows a desired trajectory, while CBFs define safety boundaries, preventing collisions between the ego-vehicle and other road users. The optimal control input is then obtained by solving a

Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin

Model-Based Design and dSPACE/MathWorks Environment Enable Innovative Engineering Education

2026-01-0073To be published on 04/07/2026

The exponentially growing complexity of engineering systems, such as a robotic system, an autonomous vehicle, and an unmanned aerial vehicle, requires sophisticated control strategies that can efficiently coordinate the system’s operation in various environments. The traditional control design approaches present significant challenges for control engineers to keep up with the increasing complexity and changing requirements. To advance embedded control system design, a paradigm shift from traditional development approaches toward more structured, systematic methodologies that can manage the multi-domain nature of control systems is critically needed. Model-based design approach is emerging as a solution for this demand. Model-based design approach uses a system model for control system development, from requirements capture to control system design, implementation, and testing. It provides an integrated environment for design, implementation, automatic code generation, and validation

Repaka, Sindhura, Chen, Bo

Ultra-Low Latency Video Pipeline Optimization for Perception-Centric Autonomous Systems

2026-01-0022To be published on 04/07/2026

Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages-sensor acquisition, video encoding, data transmission, decoding, and display-undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software

Indrakanti, Rama Kiran Kumar

Autonomous Racing Control Using Reinforcement Learning with Racing Line Guidance

2026-01-0031To be published on 04/07/2026

In recent years, with the emergence of autonomous racing competitions such as Roborace, autonomous racing cars have become a prominent research focus and have undergone rapid development. This paper establishes a reinforcement learning-based decision-making and control framework for autonomous racing cars, achieving cooperative optimization between high-speed racing and safety control under extreme operating conditions. By integrating a Control Barrier Function constrained reward function for dynamic stability and a racing line-guided path optimization, the proposed framework achieves a synergistic balance between minimum lap time and operational safety. Dynamic stability domain constraints based on a two-degree-of-freedom single-mass rigid-body model and racing line optimization based on track geometric modeling are established. The phase plane stability boundary is constructed using the yaw rate method and double-line method, upon which Control Barrier Function constraints are

peng, tao, Zhang, Weiqi, Li, Zengwen, sudong, jiang

Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation.

2026-01-0050To be published on 04/07/2026

The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) Systems, especially at higher automation levels such as SAE Level 3 or 4, demands the testing of a vast array of scenario variants far exceeding the scope of standard safety specifications like Euro NCAP (The European New Car Assessment Programme). Autonomous vehicles require thorough real-world testing to ensure automotive safety. However, public road tests are costly and risky. Instead, virtual scenarios - digital twins of real environments - offer a safe, cost-effective testing alternative. Exhaustive simulation across this high-dimensional scenario space, which includes variations in actor behavior, environmental conditions, and event characteristics, is computationally infeasible. We propose a constraint-solving approach to address this challenge that leverages mathematical and geometric techniques to analytically assess the existence and validity of scenario variants prior to simulation. Two

Karve, Omkar, Saurav, Saket, Purwar, Prabhanshu

Developing Safety Case for Automated Driving following UL4600 & ISO5083

2026-01-0527To be published on 04/07/2026

With the increasing in probabilistic outcomes by using complex algorithms/trainings for autonomous features, the safety of the product becomes more comprehensive where the safety case is one of claims to the overall safety assurance. Nonetheless, the safety case still needs to follow rigorous processes and provide all critical safety deliverables. Newer and updated standard such as UL4600 and ISO5083 have more considerations and claims can include in the safety case. From the definition of the autonomous feature to the risk acceptance criteria to the verification and validation of these features can all have elements from those new standards. ISO 5083 provides a framework for an autonomous system which can align with both ISO26262 and 21448. The scope and the fallback maneuvers can complement the hazards identified through safety analysis. The risk acceptance covers both Functional Safety and SOTIF through validation of potential metrics. Similarly, the evidence sufficiency and risk

Mudunuri, Venkateswara Raju, Almasri, Hossam, Fan, Hsing-Hua

OFEODM: An Onboard Fast and Efficient Object Detection and Distance Prediction Model

2026-01-0017To be published on 04/07/2026

Object detection and distance prediction have advanced significantly in recent years. The YOLO series has released its 11th version, along with numerous variants that have been applied across various fields. Meanwhile, the Detection Transformer (DETR) has repeatedly set new state-of-the-art (SOTA) records in the field of object detection. Depth Anything also released its second version last year, further pushing the boundaries of distance detection. Although these models achieve impressive performance, they often require substantial computational resources. However, for algorithms intended for real-world applications and deployment on onboard devices, computational resources are limited and valuable. Inference time per frame is a critical factor in ensuring an algorithm's reliability and feasibility. Designing a model that operates in real time without sacrificing accuracy remains an extremely challenging problem, and extensive research is ongoing in this area. To address this

Li, Taozhe, Wang, Hanchen, Hajnorouzali, Yasaman, Xu, Bin

Navigation Integrated End-to-end Path Planning Development and Vehicle Testing

2026-01-0032To be published on 04/07/2026

In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes and lighting conditions, and it shows improved route adherence in vehicle testing. The proposed model is validated in a comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test

Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin

Attention and Intrinsic Curiosity-Enhanced Deep Reinforcement Learning for Path Planning in Dynamic Environments

2026-01-0044To be published on 04/07/2026

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

Shen, Shiquan, Liu, Jiahao, Chen, Zheng, Li, Zongdian, Zhao, Yuting, Wu, Minggong, Zhao, Jie, Qin, Zongquan, Wang, Yanfeng

Securing the AI-Driven Vehicle: A Hardware-Based Security Framework for Scalable, Trustworthy Autonomy

2026-01-0085To be published on 04/07/2026

Autonomous driving technology in modern vehicles marks a pivotal evolution in transportation, but it may also introduce system-level vulnerabilities that span from tampering with sensors and interfaces to compromising compute units and communication links. This paper proposes a unified, layered hardware security architecture for AI-equipped automated vehicles. Based on current automotive Ethernet and zonal architectures, it provides end-to-end trust using hardware interface security, accelerated-cryptography, and SRAM PUF-based key provisioning. All security primitives are anchored to a hardware root of trust, delivering cryptographic identity, secure boot enforcement, and trusted key storage across the entire vehicle lifecycle. Our architectural assumptions follow the direction ISO 26262, ISO/SAE 21434 and AI development mandates for next-generation SAE Level 3+ vehicles using zonal Ethernet backbones, Time-Sensitive Networking (TSN), and heterogeneous compute islands (e.g., vision

C Suriyanarayanan, Pav, Iacob, Radu

A Real-Time Repositioning Strategy for SAEV Fleets Using a Simulation-Informed Deep Learning Model

2026-01-0035To 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

StreamPETR-TKD: Temporal Knowledge Distillation for Efficient 3D Object Detection in Object-Centric Streaming Models

2026-01-0023To be published on 04/07/2026

Bird's-Eye-View (BEV) perception has become pivotal in autonomous driving for 3D object detection, enabling the transformation of multi-view image features into a unified BEV representation. However, purely visual methods still struggle with depth estimation and temporal consistency, particularly in modeling long-term dependencies and avoiding trajectory confusion in dynamic scenes. Knowledge distillation (KD) offers a promising solution by transferring rich knowledge from a powerful teacher model to a efficient student model, enhancing performance without incurring significant computational overhead. This paper aims to enhance the temporal reasoning and geometric understanding of the StreamPETR model—an object-centric, streaming-based 3D detector—via a multi-level distillation strategy. 1.Temporal Relation Distillation: The self-attention mechanisms of the teacher model are distilled to impart temporal relational knowledge, enabling the student to associate object states across time

Development of technology to distinguish contaminants using LiDAR near-field point cloud based on real-field data

2026-01-0020To be published on 04/07/2026

Sensing technologies are essential to autonomous driving, enabling environmental perception and supporting navigation and its decision-making. However, most publicly available datasets are collected under perfect weather conditions and fail to capture the challenges of real-world environments. To address this gap, we built a large-scale dataset from extensive driving campaigns across diverse regions of the United States, covering conditions such as rain, snow, dust, clear weather and anti-ice material affects. Data were acquired through a multimodal sensing devices - including perception cameras, LiDAR, infrared sensors, and external monitoring systems - providing a comprehensive representation of the vehicle's operational environment. Building on this dataset, our study focuses on LiDAR data, emphasizing contaminant classification as a critical requirement for sensor-cleaning strategies essential to reliable autonomous driving. Using raw LiDAR signals collected over 22,000km of

Advanced Tire Looseness Identification through Algorithmic Gear Error Analysis and Probabilistic Assessment

2026-01-0590To 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 Fault-Tolerant Control Strategy for Drive System Failure of Corner Module-by-Wire Chassis Vehicles

2026-01-0620To be published on 04/07/2026

As an emerging research focus, corner module-by-wire chassis vehicles address the pain points of traditional chassis in flexibility, cost, and development efficiency, and have become one of the key infrastructures in the autonomous driving era. However, their large number of actuators significantly increases the risk of failures. This paper analyzes the characteristics of such vehicles and studies the fault-tolerant control for their drive system failures. Firstly, a full-vehicle dynamic model was established, with mathematical modeling conducted for the vehicle body, motor, tire, and corner module system models. Secondly, a hierarchical yaw stability control strategy was designed for the non-faulty actuator scenario: the decision-making and control layer adopted both Sliding Mode Control (SMC) and fuzzy PID control, selecting the method with better performance to output the additional yaw moment; the control allocation layer used a quadratic programming algorithm based on tire load

Zheng, Hongyu, Zhang, Tianhao, Zhang, Yuzhou

Evaluating Cloud Platforms for Real-Time Cooperative Perception Systems Using RSU Cameras: A Comparative Study of Google Colab, Azure, and AWS

2026-01-0018To 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

AI model creation and System Verification of “Telemotion” Infrastructure-Cooperative Autonomous Driving System using Digital Twin

2026-01-0255To be published on 04/07/2026

The concept of the vehicle has changed as a result of many innovations over the last decade in the fields of connected, autonomous/automated, shared, and electric (CASE) technologies. At the same time, labor shortages in Japan are becoming more serious due to a decline in the working population. To help resolve these issues, a remote-controlled autonomous vehicle driving system called Telemotion has been developed that automates the movement of vehicles in production plants. Telemotion is an autonomous driving and transportation system in which the recognition, judgment, and operation functions of driving are handled by a control system outside the vehicle that communicates wirelessly with the vehicle. This system utilizes artificial intelligence (AI) and other advanced technologies to realize safe unmanned autonomous driving, and is already in operation in production plants. Currently, efforts are under way to build a digital twin environment and conduct AI learning using computer

Hatano, Yasuyoshi, Iwazaki, Noritsugu, Nagafuchi, Yuhei, Iwahori, Kento, Tanaka, Atsushi, Uezu, Satoru, Kanou, Takeshi, INOUE, Go, Okamoto, Yuki, Oka, Yuhei, Kakuma, Daisuke, Chiba, Hiroya, Egashira, Kazuki, Ishikuro, Megumi, Sawano, Takuro

Agile Safety Case Assessments for Autonomous Vehicle Fleets

2026-01-0521To be published on 04/07/2026

The large-scale deployment of autonomous vehicles (AVs) demands not only robust safety cases but also systematic approaches for their assessment. A safety case is a structured argument, supported by evidence, that a system is acceptably safe within its intended domain. While many AV developers are now adopting safety cases as a prerequisite for launch readiness, effective assessment methods have not kept pace with the field’s rapid progress. This paper presents a three-stage methodology for safety case assessment—framework, solution, and implementation assessment—developed through nearly fifty evaluations across commercial and defense AV programs. Framework assessment benefits from leveraging safety case frameworks, which accelerate development and prevent argument defects. Solution assessment focuses on defining clear evidence criteria, while implementation assessment evaluates the sufficiency of complete evidence packages. Together, these stages support iterative improvement and

Wagner, Michael

ENERGY AND OPERATIONAL EFFICIENCY OF SHARED AUTONOMOUS FLEET

2026-01-0464To be published on 04/07/2026

Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The test aims to verify the

Tang, Kang, Abdulsattar, Harith, Yang, Hao, Wang, Jinghui

CrashML Analyzer: A Data-Driven Approach for Investigation and Fault Assignment in Autonomous Driving Collisions

2026-01-0160To be published on 04/07/2026

This study investigates factors contributing to autonomous vehicle (AV) accidents and proposes an automated fault determination framework. A total of 563 accident reports from the State of California Department of Motor Vehicles spanning from 2019 to 2024 were analyzed by converting unstructured standardized reports into structured data using custom extraction tools. Analysis of these reports reveal that AVs were not at fault in 69.4% of cases, and were fully at fault for 22.6% of the cases. The proposed method uses these reports to provide an early indicator of fault likelihood and potentially replaces tedious manual review. Machine Learning (ML) and Natural Language Processing techniques were used to replicate the reported faults, achieving 96% average accuracy across three models: Gradient Boosting, Linear Regression, and Random Forest. Through feature engineering techniques in semantic feature extraction from narrative accident descriptions, quantifiable variables were obtained and

Rwejuna, Florida Perfect, Majid, Nishatul, Goutham, Mithun, Loukili, Alae

Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis

2026-01-0170To be published on 04/07/2026

Vision-language models (VLMs) offer a promising end-to-end approach for autonomous driving, but their robustness to physical adversarial attacks remains an unaddressed gap for safety-critical deployment. This paper introduces a systematic framework for evaluating these attacks using the Dolphins VLM and CARLA simulator. We employ Natural Evolution Strategies (NES), a black-box optimization method, to generate adversarial patches without internal model access. Our approach uses semantic similarity loss and Expectation over Transformation (EoT) to ensure physical realizability under varying viewing conditions. Patches are strategically placed on realistic advertising infrastructure (bus shelter panels and highway billboards), reflecting a plausible threat model. We establish two complementary scenarios: crosswalk pedestrian detection suppression and highway steering manipulation. Our evaluation reveals critical vulnerabilities. Adversarial patches achieved a 76.0% overall attack success

Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.

THE IN-VEHICLE CONNECTIVITY FOUNDATION POWERING AUTONOMOUS DRIVING

2026-01-0009To be published on 04/07/2026

Achieving full vehicle autonomy is not just about adding sensors or compute-it requires a fundamental shift in how vehicles are architected. Autonomous systems rely on higher-resolution sensors, massive processing power, and the ability to fuse data from multiple sources in real time. Centralized in-vehicle architectures, which consolidate computing and enable sensor fusion, place unprecedented demands on connectivity. Precise time synchronization across all systems becomes critical, as does advanced control to ensure safe and reliable operation. Any delay or data loss can impact decision-making, making robust, resilient communication links essential. High-performance connectivity is the backbone of this evolution. It must deliver the highest bandwidth to handle massive streams of sensor data, support long-reach connections across the vehicle, and maintain error-free performance even in the most challenging electromagnetic environments. This combination of speed, reach, and reliability

Shwartzberg, Daniel

Trajectory Prediction Model for Autonomous Vehicles in Highway Scenarios Based on Spatiotemporal Interaction Feature Extraction

2026-01-0027To be published on 04/07/2026

Accurately predicting the future trajectories of surrounding vehicles is one of the core tasks in autonomous driving, and its precision is directly related to the safety and reliability of decision-making, path planning, and control execution. However, challenges such as the complexity of traffic participants’ behaviors, the variability of interactions, and the highly dynamic nature of traffic environments make it difficult for existing methods to effectively model spatiotemporal dependencies and achieve accurate long-term prediction in dynamic scenarios, thus limiting their applicability in real-world settings. In this paper, we propose a Transformer-based trajectory prediction model with a spatiotemporal attention mechanism to extract and effectively model vehicle motion and spatial interactions. Specifically, the temporal attention module captures the motion patterns of the target vehicle across the time dimension, while the spatial attention module constructs vehicle interactions

Zhang, Lijun, Hu, Xingyu, Meng, Dejian, Zhu, Zhehui

Potential Regulatory Approaches For Occupant Safety In Automated / Autonomous Vehicles With Unique Seating Configurations – Part 2: Interior Safety Sensing and Messaging Proposals

2026-01-0576To be published on 04/07/2026

This paper contains Part 2 of a three-part paper series proposing potential regulatory approaches for occupant safety in Automated / Autonomous Vehicles (AVs) with unique seating configurations (stagecoach and campfire seating). Part 2 focuses on interior safety sensing, associated messaging, and ride control approaches both prior to and during a ride. Assessments are also proposed after significant vehicle braking and crash events. The proposed conditions are to be assessed in a static vehicle environment with people segmented by occupant size and an infant dummy. On the vehicle seat and on the vehicle floor occupant detection conditions are proposed along with restraint usage detection conditions for vehicle seatbelt usage, Child Restraint Seat (CRS) usage, CRS seatbelt usage, and Lower Anchors and Tethers for Children (LATCH) system usage. These conditions may be detected by sensors / computer algorithms and human monitoring and thus are technology agnostic. The topics of animal

Use inertial sliding of reclined seat to enhance occupant retention under rear-end crash

2026-01-0563To be published on 04/07/2026

Smart cabin of autonomous vehicle may lead more passengers to recline their seat for comfort. However, reclined seat is disadvantageous for holding occupant in seat when vehicle is subjected to rear-end crash. Under severe rear-end crash and large reclining angle, the backward inertia could completely throw occupant out of seat. Or, even if occupant body can be restrained well by seatbelt, the occupant head could slide out of the head restraint area. Any of these situations may potentially cause severe injuries. To address this safety concern, we developed a sliding seat system designed to enhance occupant retention. Activated by impact inertia from rear-end collision, the system allows the seat sliding backward along its track in a controlled manner, and the sliding stroke is accompanied by a restraint force and absorbs some amount of kinetic energy during the sliding. Meanwhile, the backward sliding of seat, together with backward sliding of occupant, makes the ride-down process more

Dai, Rui, Zhou, Qing, Puyuan, Tan, Shen, Wenxuan

RRT*_Heuristic_Adaptiv_maxR: A practical path planning method based on novel adaptive framework for autonomous mobile agent

2026-01-0038To be published on 04/07/2026

Aim at addressing the problem of random sampling and low efficiency of rapidly-exploring random tree (RRT) path planning algorithm, a practical path planning method is proposed for autonomous mobile agents, named RRT*_Heuristic_Adaptiv_maxR. This method is based on a new adaptive framework, balancing the search efficiency and convergence accuracy of path planning by dynamically adjusting the maximum radius parameters in the search process. Combining the heuristic search strategy, guiding the sampling direction, accelerating convergence to the target area, the obstacle detection mechanism is used to optimize the path in real time to avoid collisions. Experimental results show that the proposed method, RRT*_Heuristic_Adaptiv_maxR, has higher planning success rate and path quality in complex environments compared with various RRT algorithms, and it is verified the effectiveness of the adaptive framework and the maximum radius dynamic adjustment strategy in mobile agent path planning.

Dong, Xiaomei, Zeng, Dequan, Petitti, Antonio, You, Jingyi, xiong, min, GAO, Letian, Jiang, Xiaomeng, Yu, Yinquan, CARBONE, GIUSEPPE, Hu, Yiming, Nawaz, Aamir, Linh, Nguyen Vu

A Comparison of Road Grade Preview Signals from Lidar and Maps

2026-01-0026To 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

Enhancing LiDAR Performance under Fog with Adaptive Multi-Echo and Feature-Based Filtering

2026-01-0011To be published on 04/07/2026

LiDAR (Light Detection and Ranging) systems are essential for autonomous driving (AD) and advanced driver-assistance systems (ADAS), providing accurate 3D perception of the surrounding environment. However, their performance significantly deteriorates under adverse weather conditions such as fog, where laser pulses are scattered by airborne particles, resulting in substantial noise and reduced ranging accuracy. This scattering effect makes it difficult to detect objects within or behind particulate matter, posing a serious challenge for reliable perception in real-world driving scenarios. To address this issue, we propose an algorithm that combines adaptive multi-echo signal processing with a feature-integrated, rule-based denoising framework to enhance LiDAR performance in noisy environments. The multi-echo approach selectively utilizes meaningful signal returns by evaluating both reflection intensity and relative echo positions. Based on predefined rules, the algorithm identifies the

Kaito, Seiya, Zheng, Shengchao, Fujioka, Ibuki, Beppu, Taro

A Control Method for Dual Motor Redundant Steer System based on Zeroing Neural Networks

2026-01-0581To be published on 04/07/2026

In the process of driving a vehicle, the steering system directly determines the vehicle's travel path and direction, serving as the physical basis for precise control in autonomous driving. Whether it is lane keeping, turning, or obstacle avoidance, high responsiveness of the steering control is required to execute the commands of the decision-making system. This study focuses on the dual-motor steer-by-wire (SBW) system, addressing the tracking issues arising from factors such as dual-motor coupling. A control strategy for coordinated dual motor drive in an autonomous driving steering system based on dual-motor redundancy is proposed. A dynamic model of the steer-by-wire system is constructed, and the software algorithm employing a zeroing neural network for the dual-motor redundant steering is used to solve the synchronization issues of the dual-motor system, based on the concept of cross-coupled control. Finally, simulation analysis was performed on the MATLAB simulation platform

Yang, Lingang, xiong, min, Zeng, Dequan, Petitti, Antonio, Nawaz, Aamir, Linh, Nguyen Vu, Hu, Yiming, Yu, Yinquan, CARBONE, GIUSEPPE, GAO, LETIAN, Jiang, Xiaomeng

Design and Performance Evaluation of a Dust Contamination Simulation Test Apparatus for Automotive Cameras

2026-01-0059To be published on 04/07/2026

In the era of Software-Defined Vehicles (SDVs), sensor integration has become increasingly prevalent, enabling advanced features such as autonomous driving and driver assistance systems. However, contamination from road dust and muddy water poses significant risks to the performance of vehicle camera sensors. Manual methods of applying dust for testing sensor durability are subject to inconsistencies and human error, potentially undermining the validity of test results. To address this, we developed an automated dust deposition apparatus specifically designed for automotive camera systems. This device enables precise and repeatable control over dust application, thereby enhancing the reliability and efficiency of the testing process. The dust contamination simulation apparatus leverages programmable parameters to consistently reproduce various environmental scenarios encountered in real-world driving conditions. Performance evaluations demonstrated that the system achieves uniform dust

A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)

2026-01-0147To be published on 04/07/2026

The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive

Sang, I-Chen, Norris, William, Patterson, Albert, Sreenivas, Ramavarapu S., Soylemezoglu PhD, Ahmet, Nottage, Dustin S.

Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles

2026-01-0148To be published on 04/07/2026

Offroad autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 6x4 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 6x4 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 6x4 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational

Dekhterman, Sam, Patterson, Albert, Sreenivas PhD, Ramavarapu, Norris, William, Soylemezoglu PhD, Ahmet, Nottage PhD, Dustin

Self-Supervised Gaussian-Based Framework for Robust Static-Dynamic Decomposition and Real-Time Urban Scene Reconstruction

2026-01-0010To be published on 04/07/2026

We propose a self-supervised Gaussian-based framework that enhances the robustness and efficiency of static-dynamic decomposition and high-fidelity reconstruction in autonomous driving scenarios. Existing approaches demonstrate promising performance but remain prone to noisy motion segmentation, overfitting on sparse LiDAR observations, and degraded accuracy in low-texture or distant regions, resulting in unstable geometry. To address these challenges, our method introduces a feature fusion module that jointly exploits image semantics and geometric cues, enabling more reliable motion prior extraction under noise and illumination variations. A cross-sequence geometric consistency constraint is further designed to regularize reconstruction across temporal and viewpoint changes, ensuring physically coherent scene representations. In addition, we formulate an efficient Gaussian parameter optimization strategy that constrains scale and normal updates, preventing unrealistic ellipsoid growth

Feng, Runlei, Wang, Ning, Zhang, Zhihao

AI LabelMate: A Context-Aware Annotation Agent for Reducing Semantic Fragmentation

2026-01-0261To be published on 04/07/2026

Robust perception systems for autonomous vehicles depend critically on high-quality labeled data, especially for off-road and unstructured environments. However, perception model performance is often degraded by 'data chaos' from limitations in automated segmentation. Foundation models like SAM2, while powerful, usually generate masks based on low-level visual cues like color and texture gradients. In complex off-road scenes, this leads to semantic fragmentation. A single object, like a moss-covered log, can be split into not only dozens of segments for its bark and moss, but also hundreds of smaller, meaningless patches based on minor color variations. This paper introduces a context-aware annotation agent to resolve this issue. Our workflow integrates a vision-language model (Florence-2) for scene understanding with a segmentation model (SAM2) for mask generation. Instead of segmenting indiscriminately, our agent leverages Florence-2 to comprehend the image holistically, localizing

Patil, Ashish, Mikulski, Dariusz, Mwakalonge, Judith, Jia, Yunyi

A Study on Partition Layouts for Configuring Independent Spaces in Autonomous Vehicles

2026-01-0480To be published on 04/07/2026

In this study, a full-cover partition experience, as applied in aircraft, was adapted for autonomous vehicles by developing layout configurations and related technical components. Due to the spatial constraints and relatively short travel distances inherent to vehicles, it was necessary to redefine the partition structure and associated functional requirements. To address this, a console-type partition concept was proposed for selective independent space configuration. A prototype suitable for in-vehicle application was developed and evaluated through a usability study. Based on the evaluation results, design criteria for the partition layout were established, along with the identification of additional functional needs. Consequently, a partition incorporating a variable armrest, cup holder, and smartphone mount was developed, resulting in the proposal of an optimized layout and structural solution. Furthermore, by simulating autonomous driving scenarios, the partition was operated to

Park, Jiseob, Jeong, JaeMoon

Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections

2026-01-0045To be published on 04/07/2026

Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show

Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin

Optimization of Road Reference Profiles based on Highway Design for Connected and Automated Vehicles

2026-01-0096To 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

Monocular 3D Perception and Lane-Aware Bird’s-Eye-View Mapping for Autonomous Driving

2026-01-0012To be published on 04/07/2026

Accurate perception of the surrounding environment is fundamental and essential to safe and reliable autonomous driving. This work presents an integrated vision-based framework that combines object detection, 3D spatial localization, and lane segmentation to construct a unified bird’s-eye-view (BEV) representation of the driving scene. The pipeline provides geometric information on object position and orientation by employing Omni3D to infer 3D bounding boxes of objects from monocular camera frames. Detections are subsequently projected onto a 2D BEV canvas, where object instances are represented with respect to the ground plane for enhanced interpretability. To complement the object-level perception, we utilized YOLOPv2 to perform lane segmentation, producing both lane masks and lane line masks in the image domain for future coordinate transformation. By adopting a pinhole camera model, the coordinate transformation of these masks from the perspective image plane into the BEV canvas

Tan, Lin, Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, Lee, Victoria, Xu, Bin, Arjmanzdadeh, Ziba

Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks

2026-01-0029To 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

Maintaining a Healthy Safety Culture in Autonomous Vehicle Development and Operations

2026-01-0528To be published on 04/07/2026

The deployment of autonomous vehicles (AVs) requires not only technical excellence but also the cultivation of a robust organizational safety culture. While safety engineering standards and assurance processes provide essential structure, a culture that prioritizes learning, accountability, and continuous improvement is critical to ensuring public trust and operational resilience. This paper explores how UL 4600, the comprehensive safety standard for the evaluation of autonomous systems, and Safety Performance Indicators (SPIs) can be leveraged to build and sustain a Just Culture within an AV company. UL 4600 emphasizes systematic hazard identification, safety case construction, and ongoing evidence generation. When applied beyond compliance, it provides a framework for embedding safety considerations into day-to-day engineering and operational decision making. By mapping UL 4600 clauses into organizational processes and linking them with measurable SPIs—such as rates of system

Wagner, Michael, Gittleman, Michele

AI-Based Prediction of Motion Sickness in Autonomous Vehicles Using Passenger Profiles and Driving Route Data

2026-01-0154To be published on 04/07/2026

Autonomous Vehicles (AV) are expected to significantly reshape travel experiences. Drivers will no longer be responsible for operating the vehicle and will instead take on the role of occupants, free to engage in personal activities such as reading or using phones. Consequently, motion sickness arises from a mismatch between the motion perceived by the body and the motion expected by the brain. Motion sickness detection in AVs has typically been approached through sensor-driven methods that monitor vehicle dynamics as well as passenger conditions, requiring continuous streams of data for reliable predictions. However, while vehicle-mounted sensors can be seamlessly integrated into the system, passenger-oriented solutions often rely on wearable devices, which may not always be practical for wide use in everyday settings. To address this gap, we introduce a ProfileFusion model, a multimodal deep learning model providing a more user-friendly solution that requires minimal user input. It

Khosravifard, Mina, Malik, Usama Zaid, Nguyen, Trang, Grosseruschkamp PhD, Marc, Lichtenberg, Frank

Safety-Constrained Control of Nonlinear Mobility Systems Using Reference Governor

2026-01-0033To 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 Learning

2026-01-0041To 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

Safety-Centered Scenario Generation for Autonomous Vehicles

2026-01-0529To be published on 04/07/2026

This paper focuses on a scenario generation framework that creates diverse, parametrized, and safety-critical driving situations to validate the safety features of autonomous vehicles in simulation. By modeling factors such as road geometry, traffic participants, environmental conditions, and perception uncertainties, the framework enables repeatable and scalable testing of safety mechanisms, including emergency braking, evasive maneuvers, and vulnerable road user protection. The framework supports both regulatory and edge-case scenarios, mapped to hazards and safety goals derived from Hazard Analysis and Risk Assessment (HARA), ensuring traceability to ISO 26262 functional safety requirements and performance limitations. The output from these simulations provide quantitative safety metrics such as time-to-collision, minimum distance, braking and steering performance, and residual collision severity. These metrics enable the systematic evaluation of evasive maneuvering as a safety

Chandra Shekar, Kiruthiga, Arab, Aliasghar

SAE TOMORROW TODAY - Building an End-to-End Autonomous Driving System

Join us as we sit down with Richard Chelminski, Senior Vice President, Head of SDV Platform, at 42dot, an autonomous driving software and mobility platform development startup from Hyundai Motor Group. We discuss how the company is pushing boundaries with its end-to-end autonomous driving system, Atria AI, which is focused on Level 4 autonomy and designed for seamless integration into consumer vehicles -- no LIDAR required! You'll also learn why camera-first and radar technologies are reshaping the industry, how software is now at the heart of vehicle development, and what the future holds for Transportation as a Service (TaaS). If you're curious about the next wave of smart, software-defined vehicles and autonomous driving innovation, this episode is a must-listen. 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

Patterson, Lori

SAE TOMORROW TODAY - Why SDV Strategy Needs Application Layer Innovation

What's really holding back software-defined vehicles (SDVs) ... and where should automakers shift their focus? Listen in as we sit down with John Wall, President of QNX, to explore how a trusted, safety-certified foundation frees OEMs to innovate faster, collaborate more effectively, and deliver differentiated vehicles. Drawing on insights from the Under the Hood: SDV Developer Report, they unpack why automakers must shift away from maintaining complex, non-differentiating software and focus instead on application-layer innovation to better define brand identity and customer experience. You'll also learn how QNX is expanding into robotics, healthcare, and industrial automation with its secure, high-performance operating system. Whether you're fascinated by autonomous cars, collaborative robots, or the future of AI in physical systems, this episode is packed with insights on innovation, safety, and the power of partnerships. We'd love to hear from you. Share your comments, questions and

Patterson, Lori

Miner’s Safety Bot: An ESP32-Based Autonomous Mobile Mine Safety Monitoring System

2/12/2026

Mining operations are important to industrial growth, but they expose the mining workers to risk including hazardous gases, elevated ambient temperatures, and dynamic structural instabilities within underground environments. Safety systems in the past, typically based on fixed sensor networks or manual patrols, fall short in accurate hazard detection amidst shifting mine conditions. The proposed project Miner's Safety Bot advanced this paradigm by leveraging an ESP 32 microcontroller as a mobile platform that integrates gas sensing, thermal monitoring, visual inspection and autonomous obstacle avoidance. The system incorporates MQ7 semiconductor gas sensor to monitor real time carbon monoxide (CO), offering detection range from 5 to 2000 ppm with accuracy of 5 ppm. Temperature and humidity are monitored through DHT11 digital sensor, calibrated to ensure reliability across the harsh microclimates in mines. Navigation and autonomous movement are enabled by Ultrasonic Sensor (HC-SR04

D, Suchitra, D, Anitha, Muthukumaran, Balasubramaniam, Mohanraj, Siddharth, Subash Chandra Bose, Rohan

Automotive Ethernet Proxy-Based Security Architecture in Vehicle Computers

2/12/2026

Modern vehicles require sophisticated, secure communication systems to handle the growing complexity of automotive technology. As in-vehicle networks become more integrated with external wireless services, they face increasing cybersecurity vulnerabilities. This paper introduces a specialized Proxy based security architecture designed specifically for Internet Protocol (IP) based communication within vehicles. The framework utilizes proxy servers as security gatekeepers that mediate data exchanges between Electronic Control Units (ECUs) and outside networks. At its foundation, this architecture implements comprehensive traffic management capabilities including filtering, validation, and encryption to ensure only legitimate data traverses the vehicle's internal systems. By embedding proxies within the automotive middleware layer, the framework enables advanced protective measures such as intrusion detection systems, granular access controls, and protected over-the-air (OTA) update

M, Arvind, Praneetha, Appana Durga, Remalli, Ravi Teja

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