Autonomous Driving Research Articles

A Systematic Survey of Transformer-Based 3D Object Detection for Autonomous Driving: Methods, Challenges and Trends

A Systematic Survey of Transformer-Based 3D Object Detection for Autonomous Driving: Methods, Challenges and Trends

Road Design and Traffic Detection Methods for Autonomous Driving Scenarios

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method.

Visual SLAM Methods for Autonomous Driving Vehicles

Visual SLAM Methods for Autonomous Driving Vehicles

Advancements and Challenges in Visual Perception for Autonomous Driving

Advancements and Challenges in Visual Perception for Autonomous Driving

Endurance to Multiple Factors of Water-Based Electrically Conductive Paints with Metallic Microparticles

The paper describes the innovative adaptation of some specific environmental tests from general organic coatings towards newly developed water-based composite paints with metallic particles (Al and Fe), with a high content of metal (10% and respectively 20%) for electromagnetic shielding applications. Electrical conductivity is the most affected dielectric parameter under both by UV radiation and thermal exposure. The paints with 20% metallic powder are more sensitive to environmental factors, and the influence of metal type could also be emphasized in relation to the dielectric feature evolution vs. exposure time. The action of mold significantly decreases the dielectric features of paints, but the weathering aging effect is much more enhanced if the samples are cumulatively submitted to thermal aging and respectively UV exposure, along with the action of mold. The potential application of the study is related mainly to the development of new autonomous electric cars, which need special conditions of electromagnetic shielding, under the circumstances that the conductive paint layers are normally very sensitive to environmental factors, affecting the equipment performance and security.

(Invited) Heavy-Metal-Free Quantum Dot Thin-Film Photodetectors for SWIR Imaging: A Comprehensive Analysis for Performance Enhancement

Various emerging technologies investigate the ability to sense and detect light in the shortwave infrared spectrum (wavelengths between 1100nm and 2500nm). Autonomous cars, smart agriculture, eye tracking for AR and VR applications, face recognition, surveillance, machine vision and new microscopy techniques can take advantage of additional information found in SWIR.Thin film photodetectors, based on quantum dot photodiodes, show promise in bringing SWIR sensing to the mass markets by offering simpler and cheaper processes and higher resolution than existing technologies. However, the prevalent use of lead (Pb), Cadmium (Cd) and mercury (Hg) in existing quantum dot thin film photodetectors poses safety, environmental and regulatory challenges, limiting their widespread adoption.InAs quantum dots emerge as a leading alternative, offering a heavy metal free solution and paving the way for the next generation of SWIR imaging devices. Nevertheless, a nuanced understanding of InAs-based devices is imperative for unlocking their full potential and optimizing performance. This work presents a meticulous and extensive analysis conducted in our laboratories, encompassing both devices and materials. The resulting insights empower an agile and informed redesign of the photodiode, showcasing tangible improvements in performance metrics.

Behavioural Gap Assessment of Human-Vehicle Interaction in Real and Virtual Reality-Based Scenarios in Autonomous Driving

In the field of autonomous driving research, the use of immersive virtual reality (VR) techniques is widespread to enable a variety of studies under safe and controlled conditions. However, this methodology is only valid and consistent if the conduct of participants in the simulated setting mirrors their actions in an actual environment. In this paper, we present a first and innovative approach to evaluating what we term the behavioural gap, a concept that captures the disparity in a participant’s conduct when engaging in a VR experiment compared to an equivalent real-world situation. To this end, we developed a digital twin of a pre-existed crosswalk and carried out a field experiment (N = 18) to investigate pedestrian-autonomous vehicle interaction in both real and simulated driving conditions. In the experiment, the pedestrian attempts to cross the road in the presence of different driving styles and an external Human-Machine Interface (eHMI). By combining survey-based and behavioural analysis methodologies, we develop a quantitative approach to empirically assess the behavioural gap, as a mechanism to validate data obtained from real subjects interacting in a simulated VR-based environment. Results show that participants are more cautious and curious in VR, affecting their speed and decisions, and that VR interfaces significantly influence their actions.

Evaluating the User Experience of V2X Communication Alerts for Emergencies in Level 3 Autonomous Driving

Communicating effectively with autonomous vehicles requires contextualized visual and auditory cues to ensure clear message delivery. Evaluating the user experience involves assessing which types of information can be safely reacted to without additional monitoring and how it is presented. Validating the visual and auditory cues supports the driver’s course of action. This study investigates message types and preferred modalities of driver-to-driver communication via vehicle-to-everything (V2X) in advanced driver assistance systems technologies for autonomous and manual driving and proposes efficient ways to respond to event situations. Four modalities, including baseline and three message types with different information were proposed to investigate the information required by drivers. Results indicate that providing notifications during autonomous driving is more helpful and less workload-intensive than during manual driving. Most notifications were highly visible and easy to recognize. Although behavioral messages in both autonomous and manual driving enhance usability, providing advice and behavioral messages is safer for autonomous driving. Designing V2X notification information based on future events is vital because of its highly pragmatic nature.

Autonomous Cars and Consumer Choices: A Stated Preference Approach

Autonomous Cars and Consumer Choices: A Stated Preference Approach

Impact of Perception Errors in Vision-Based Detection and Tracking Pipelines on Pedestrian Trajectory Prediction in Autonomous Driving Systems.

Pedestrian trajectory prediction is crucial for developing collision avoidance algorithms in autonomous driving systems, aiming to predict the future movement of the detected pedestrians based on their past trajectories. The traditional methods for pedestrian trajectory prediction involve a sequence of tasks, including detection and tracking to gather the historical movement of the observed pedestrians. Consequently, the accuracy of trajectory prediction heavily relies on the accuracy of the detection and tracking models, making it susceptible to their performance. The prior research in trajectory prediction has mainly assessed the model performance using public datasets, which often overlook the errors originating from detection and tracking models. This oversight fails to capture the real-world scenario of inevitable detection and tracking inaccuracies. In this study, we investigate the cumulative effect of errors within integrated detection, tracking, and trajectory prediction pipelines. Through empirical analysis, we examine the errors introduced at each stage of the pipeline and assess their collective impact on the trajectory prediction accuracy. We evaluate these models across various custom datasets collected in Taiwan to provide a comprehensive assessment. Our analysis of the results derived from these integrated pipelines illuminates the significant influence of detection and tracking errors on downstream tasks, such as trajectory prediction and distance estimation.

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Evolutionary End-to-End Autonomous Driving Model With Continuous-Time Neural Networks

Evolutionary End-to-End Autonomous Driving Model With Continuous-Time Neural Networks

Review of Accident Detection Methods Using Dashcam Videos for Autonomous Driving Vehicles

Review of Accident Detection Methods Using Dashcam Videos for Autonomous Driving Vehicles

Guard-Net: Lightweight Stereo Matching Network via Global and Uncertainty-Aware Refinement for Autonomous Driving

Guard-Net: Lightweight Stereo Matching Network via Global and Uncertainty-Aware Refinement for Autonomous Driving

Boundary State Generation for Testing and Improvement of Autonomous Driving Systems

Boundary State Generation for Testing and Improvement of Autonomous Driving Systems

IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

End-to-End Autonomous Driving Decision Method Based on Improved TD3 Algorithm in Complex Scenarios.

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability.

Real Time Traffic Sign Recognition Using Integrated Camera Sensors and YOLOV8 Algorithm

Traffic signs are an important element in maintaining smoothness and safety on the road. However, there are still many drivers who violate them, causing various negative impacts. Traffic Sign Recognition (TSR) is a technology that has the ability to detect and identify various types of traffic signs by utilizing artificial intelligence in the computer vision domain. TSR has been applied in various vehicle applications, such as Advance Driver Assistance System (ADAS) and Autonomous Driving System (ADS). This system is made by integrating camera sensors with the YOLOv8 algorithm which has high accuracy and fast data processing. The data used were 2093 images and annotated through Roboflow. Then the data is trained through Google Collaboratory with a mAP evaluation result of 95.5% which shows that the system can detect objects accurately. The level of precision and success of the model in detecting objects is 93.5% and the success is 93.3%. The results of system testing can use images, videos, and through cameras in real time.

Multi-Source Data Integration for Navigation in GPS-Denied Autonomous Driving Environments

Multi-Source Data Integration for Navigation in GPS-Denied Autonomous Driving Environments

Localization robustness improvement for an autonomous race car using multiple extended Kalman filters

In this paper, we introduce a vehicle localization method designed for the SZEnergy race car, which competes in the Shell Eco-marathon. The proposed method comprises four different extended Kalman filter-based localization algorithms and a selection algorithm that determines the most suitable one based on vehicle speed, GNSS availability, and signal quality. The low-speed Kalman filters are based on a kinematic vehicle model while the high-speed variants are based on a dynamic vehicle model. Several measurements were performed during test maneuvers to evaluate the performance of the filters. The proposed method succesfully handles sensor miscalibration and GNSS outages.