AUTONOMOUS DRIVING IEEE PAPER 2022



Automation can help reduce the number of crashes on our roads. Government data identifies driver behavior or error as a factor in 94 percent of crashes, and self-driving vehicles can help reduce driver error. Higher levels of autonomy have the potential to reduce risky and dangerous driver behaviors.
Autonomous driving normally refers to self-driving vehicles or transport systems that move without the intervention of a human driver
Level 0: No Autonomy.
Level 1: Driver Assistance.
Level 2: Partial Automation.
Level 3: Conditional Automation.
Level 4: High Automation.
Level 5: Full Automation


Cruise, Tesla, Alphabet Inc's (GOOGL. O) Waymo and Aurora Innovation Inc (AUR. O) are among many companies aiming to deploy fully autonomous vehicle technology in the United States within the next two to three years, whether or not federal regulators give them a clear legal framework for doing so






Autonomous Driving System (ADS). It receives information from the external environment and then makes driving methodology for protecting Autonomous Driving Systems with Lifelong

Personalized Car Following for Autonomous Driving with Inverse Reinforcement Learning
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Driving automation is gradually replacing human driving maneuvers in different applications such as adaptive cruise control and lane keeping. However, contemporary driving driving

Learning Mixture of Domain-specific Experts via Disentangled Factors for Autonomous Driving
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Since people know the important factors that affect the control based on the driving situation, they can drive safely even in diverse driving environment. To mimic these behaviors, we

Critical Scenario Identification for Realistic Testing of Autonomous Driving Systems
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autonomous driving functions. We also demonstrate the effectiveness of our approach by using two real autonomous driving is general for testing different autonomous driving systems.

Enabling Safe Autonomous Driving in Uncertain Environments
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Autonomous driving technologies have been developed in the past decades with the guarantees for autonomous driving applications comes from the fact that the self- driving vehicle

Who Will Be the Orchestrator in An Autonomous Driving (AD) Business Ecosystem -The Position of the
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As one of the most strategically significant and financially promising developing industries, autonomous driving (AD) ecosystems are facing challenging technical obstacles,

Sensor Data Validation and Driving Safety in Autonomous Driving Systems
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driving safety evaluation framework to investigate the impact of adversarial attacks on the driving safety of autonomous and security of autonomous driving technology and eventually

YOLOv3: Traffic Signs Lights Detection and Recognition for Autonomous Driving
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network for an autonomous navigation robot is presented in (Moura et al.). This project aimed to participate in the Portuguese RoboCup Open Autonomous Driving Competition . It

Combining YOLO and Deep Reinforcement Learning for Autonomous Driving in Public Roadworks Scenarios
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Autonomous driving is emerging as a useful practical AI is a well-known solution for some autonomous driving problems but be transported to the real-world for secure and safe driving .

Cyber-security oriented co-simulation platform for connected and autonomous driving
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of connected and autonomous driving is required before the operation of connected and autonomous vehicles by offering of real-world connected and autonomous vehicles. Moreover,

Multiple road user interactions: a methodological approach towards autonomous driving
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In Autonomous Vehicle Design, the biggest challenge is integrating the human factor into autonomous driving . Whether on the driver’s seat of a self- driving car, or crossing the road in

An Efficient Framework for Reliable And Personalized Motion Planner in Autonomous Driving
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measures the performance of autonomous driving cars in terms Both of them evaluate the behaviors of autonomous driving motion planner performance in autonomous driving which re

Letter from the Special Issue Editors: Special Issue on Autonomy and Connectivity at the Edge Autonomous Racing,
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of autonomous driving grow in importance. Companies like Tesla, Waymo, and Zoox are now well established and provide their autonomous driving drive with autonomous vehicles in

SIMULATION SCENARIOS IN SELF- DRIVING CARS
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and autonomous systems. Can recreate real-world driving conditions to test systems under variable traffic, terrain, weather and lighting yet for autonomous driving SimulationCity

Motion-aware vehicle detection in driving videos
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can assist in driving scenarios since driving is a driving videos which enables the model to be used in time-critical applications such as autonomous driving and advanced driving

From unmanned systems to autonomous intelligent systems
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Autonomous driving (aka, self- driving ) is an extremely popular area of research that explores the roots of autonomy and intelligence of a self- driving topic of autonomous driving . Self-

How automated decision-making can increase the safety of connected and autonomous vehicles
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When networks scale up, for example on busy roads where autonomous driving A WDLC model has the potential to achieve ultra-reliable joint decisions in autonomous driving despite

Redundant Multi-Object Detection for Autonomous Vehicles in Structured Environments
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This paper presents a redundant multi-object detection method for autonomous driving exploiting a combination of Light Detection and Ranging (LiDAR) and stereocamera sensors to

Integration Stability Analysis Of Nonlinear Model Predictive Motion Control For Autonomous Vehicles
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of autonomous driving . However, it is odd that integration stability has not been considered enough when developing these applications for autonomous driving of autonomous vehicles (

MECHANISM OF ROAD CAPACITY UNDER DIFFERENT PENETRATION SCENARIOS OF AUTONOMOUS VEHICLES
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(2) Autonomous car-following model Autonomous driving is to replace the driver to control the vehicle through the automatic driving technology, and the realization of the control

Using Adaptive Stress Testing to Identify Paths to Ethical Dilemmas in Autonomous Systems
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We first define them as situations in which an autonomous agent can only choose from autonomous system gets into an ethical dilemma. Using an example from the autonomous driving

Inverse Reinforcement Learning with Hybrid-weight Trust-region Optimization and Curriculum Learning for Autonomous Maneuvering
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We propose an efficient and novel mediated-perception framework for autonomous driving that exploits contextaware multi-sensor perception for inverse reinforcement learning with

Reinforce Learning-Based Collision Avoidance in Network Assisted Automated Driving
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and efficient driving capabilities. It is a new generation of autonomous driving technology with great potential, and it is also the future development trend of autonomous driving .

Autonomous Robotic Self- Driving Cars 2021
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Automated Driving Toolbox, from Matlab R2021b, provides algorithms and tools for designing, simulating, and testing autonomous driving systems. It is possible to design and test

Development of Real-Time Traffic-Object and Traffic-Sign Detection Models Applied for Autonomous Intelligent Vehicles
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In these lowest autonomous driving levels, drivers have to monitor the driving situations continuously to perform interventions. In contrast, from level 3 on, the autonomous driving

Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)
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autonomous systems and adopting safety-critical control into the domain of autonomous driving How to improve the expressivity of autonomous vehicles’ safe-critical controllers, so that

Usage Patterns and Perceptions of Shared Autonomous Vehicles (SAVs): Empirical Findings from a Self- driving Service
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Technology-driven mobility services such as shared autonomous vehicles (SAVs) are gaining in popularity, and many cities in the world are implementing pilot projects that provide

Fixed Settling Time Control for Self- Driving Car: Two-Timescales Approach
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In recent times self- driving cars have undergone rapid 12 advancements with the evolution driving cars are demonstrated 14 by academia and industry through Google self- driving car,

Autonomous Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee using Control Barrier Functions
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Recently, autonomous racing is an active subtopic in the field of autonomous driving research. In autonomous racing, the ego car is required to drive along a specific track with an autonomous parking methods have been implemented in cars like Audi A Tesla model S, Ford expedition, etc. With an increasing demand for electric cars, fully autonomous driving

REAL-TIME SINGLE-CAMERA VEHICLE TO VEHICLE DISTANCE MEASUREMENT FOR AUTONOMOUS VEHICLE
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vehicle in front is very important when driving . A driver must always be autonomous vehicles, measuring vehicle to vehicle distance is very important as a benchmark for autonomous

Emergency Collision Avoidance Strategy for Autonomous Vehicles Based on Steering and Differential Braking
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standard associate system on most autonomous vehicle (AV) last decade for highway autonomous driving and discussed their to enhance driving stability. In this research line, the four-

Behavior Cloning for Self Driving Cars using Attention Models
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and MAPs for any self- driving or autonomous systems for getting considerable efficiencies. The in an autonomous driving car. In this paper behavioral cloning of an autonomous car is

Data-Driven Security Verification for Autonomous Debris Removal in Space
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For example in autonomous driving road traffic signs or pedestrians can be added to the point cloud data to confuse the autonomous driving module and cause accidents or faults.

Multi-directional-motion-coupling-based Extreme Motion Control of Distributed Drive Autonomous Vehicle
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reaches the extreme of stable driving the working condition is As one of the key technologies to realize autonomous driving and changeable driving conditions of autonomous vehicles,

Digital Light for Digital Live
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While driving this evolution of connected machines, it is also a healthy acceptance of autonomous driving vehicles. For this offers features that make driving easier and more secure.

Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions
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such as night-time driving or situations involving high Considering the domain of autonomous driving which primarily Current object-detection models for autonomous driving lack thein both manual and autonomous driving . While the sensors ’s technical status, driving performance and driver’s condition, complex driving support system which determines the driving

Situation-aware Drivable Space Estimation for Automated Driving
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Jos Elfring is an assistant professor at Eindhoven University of Technology and a researcher at the TomTom product unit Autonomous Driving . He received his MSc in Mechanical

An Open-source Library for Processing of 3D Data from Indoor Scenes
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of applications, from robotics to autonomous driving . One of these applications is the processing of 3D indoor scenes, usually in the context of autonomous navigation of mobile robots,

Fusion of Different Features by Cross Cooperative Learning for Semantic Segmentation
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Especially in autonomous driving it is necessary to instantly predict various classes autonomous driving . In this paper, we work on semantic segmentation task for autonomous driving .

Towards an Accountability Framework for AI: Ethical and Legal Considerations
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Autonomous driving is arguably one of the most elaborate existing research areas. Many scientific disciplines and technologies are needed to realize the dream of a fully self-

Multi-lane Modelling using Convolutional Neural Networks and Conditional Random Fields
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Over the years, progress in autonomous driving has grown due autonomous driving vehicle issues has expanded the horizons and brought the vehicles to reality. Autonomous Driving

An Adaptive Neuro-Fuzzy Inference System to Solve Perceptual Aliasing for Autonomous Mobile Robots
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The ever increasing applications of mobile robots have made it one of the most active fields of research. Simultaneous Localization and Mapping (SLAM) is one of the core problem of

Research on Smart Regulation of Intelligent Connected Vehicles
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and exhaust emissions of autonomous vehicles. NHTSA will conduct research on alternative indicators and safety assessment models for autonomous driving . The European

High-Precision Position Protocol for Vehicle to Pedestrian using 5G Networks
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reliability that cannot be increased only by autonomous driving technology using in-vehicle autonomous driving control by expanding the recognition range of autonomous vehicles

Whose Track Is It Anyway Improving Robustness to Tracking Errors with Affinity-based Trajectory Prediction
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making in human-interactive autonomous systems, such as self- driving cars. However, most Experiments on large-scale, real-world autonomous driving datasets show that our affinity-

Laneformer: Object-aware Row-Column Transformers for Lane Detection
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autonomous driving . The dominant paradigms rely on purely CNN-based architectures which often fail in incorporating relations of long-range lane points and global contexts induced

Object Detection of Road Facilities Using YOLOv3 for High-definition Map Updates
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Autonomous driving technology is currently being developed owing to industry demand for a more robust detection system to ensure safety during driving . of a driving vehicle equipped

Tuning MPC Path-following Controllers Using Multi-objective Optimization
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Abstract This paper presents a design approach for tuning autonomous driving controllers been applied to the tracking-control lateral of autonomous vehicles. Additionally, in order to

3D Detection of Vehicles from 2D Images in Traffic Surveillance
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Although KITTI data focuses on autonomous driving this will provide insight in the differences with our application. The KM3D detector is trained independently on KITTI, our dataset,

Systematic Model-based Design of a Reinforcement Learning-based Neural Adaptive Cruise Control System
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(Holistic Electronic Vehicle Management for Autonomous Electric Vehicles) focuses on the and functions for autonomous driving . With a higher degree of automation in driving the

Lidar-Camera Fusion For Road Detection Using Recurrent Conditional Random Field Model
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Autonomous vehicles are often equipped with multiple improve the perception robustness in autonomous vehicles. to analyze the sensor inputs for autonomous driving the image and

Mental workLoad Accumulation Effect of Mobile Phone Distraction in L2 Autopilot Mode
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autonomous driving autonomous driving mode is also a focus that this study explores. A recent study shows that the rst 40 minutes of a driving task under monotonic autonomous driving

Parabolic Model Based Lane Detection and Tracking
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Abstract- Lane detection is the initial and main key role in the context of Advanced Driver Assistance Systems (ADAS) and autonomous driving in the case of complex and challenging

Retriever: Point Cloud Retrieval in Compressed 3D Maps
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Abstract Most autonomous driving and robotic applications require retrieving map data around the vehicle’s current location. Those maps can cover large areas and are often stored

Generating High Resolution Depth Image from Low Resolution LiDAR Data using RGB Image
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In recent years, autonomous driving and driving support for Especially in autonomous driving it is expected that LiDAR will be In order to realize autonomous driving it is important to the autonomous driving data as well as indicators that are characteristic of driver aggression. In order to draw such insights from the driver’s behavior, the application is modeled using

The Empathetic Car: Detecting Emotion and Well-being of Drivers under Naturalistic Condition
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Given the current development progress, autonomous driving features are mostly available in traffic; compared with autonomous driving systems that completely overtake driving tasks,

Data Fusion for Automated Driving : An
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for which lane or which traffic participant is driving on which lane. Relational states can be including the estimated intention/behavior/ driving maneuver states of other traffic participants (

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