Collision Warning Research Articles

Side Collision Detection Model for Visually Impaired Using Monocular Object-specific Distance Estimation and Multimodal Real-World Location Calculation

Targeting the potential risk of side-vehicle collisions when the visually impaired crosses roads, this study proposed a side collision detection model, including monocular distance estimation, multimodal real-world location estimation, future location prediction and collision warning strategies tailored for visually impaired pedestrians. The proposed model employs YOLOv8 and DeepSort for vehicle detection and tracking, utilizing shallow neural networks for distance estimation based on image information and vehicle position data. Predicted vehicle distances are combined with magnetic field sensor and GPS data to compute and store real-world vehicle locations, and these location data will be used for linear regression to forecast future locations. A warning strategy is then implemented to alert users. Experimental validation shows that the monocular distance estimation network has an Absolute Relative Error of 0.043 and an ALE (Average Localization Error) of 1.249m. In real-world location estimation, the view angle ALE is 0.019, and the location ALE is 1.778m. Regarding location prediction, the accuracy in distinguishing stationary and moving vehicles reaches 0.962, and the predicted curve, based on ground truth and predicted locations, exhibits good alignment. The proposed warning strategy, evaluated on Kitti Tracking Dataset and a self-created dataset, accurately detects the majority of potential collision risks.

Attitude inversion of space debris based on the laboratory-tested photometry dataset

The attitude of space debris holds significant implications for active debris removal and collision warnings. This study constructs a comprehensive photometry dataset for the target, utilizing angle transformation relationships and drawing upon the CZ-4C photometric data tested in the laboratory. The photometry in the dataset with noise and translation simulates the telescope observed one. We use the genetic algorithm to search for attitude parameters of the photometry that matches the simulated photometry in the dataset under the constraint of attitude angle rules. The derived and preset values of attitude parameters exhibit a strong alignment, applicable to light curves with an amplitude of about 2.5 magnitudes and noise levels up to 1.0 magnitudes. This suggests that the algorithm’s accuracy is validated against the preset values, demonstrating its robust resistance to noise. Furthermore, the algorithm proposed in this study for deriving attitude from photometry requires a small amount of photometry data. This study provides a promising algorithm for future attitude analyses from telescope-observed photometry.

Vehicle to Vehicle Communication Using Li-Fi Technology

Abstract: Vehicle-to-vehicle (V2V) communication has emerged as a pivotal technology in the automotive industry, enabling enhanced safety, efficiency, and convenience on the roads. This abstract explores the integration of Light Fidelity (LiFi) technology in V2V communication systems. LiFi, utilizing visible light communication (VLC), offers high-speed wireless connectivity by using light-emitting diodes (LEDs) to transmit data. By harnessing LiFi's unique advantages such as increased bandwidth, low latency, and immunity to radio frequency interference, V2V communication systems can facilitate real-time exchange of critical information between vehicles, including traffic conditions, collision warnings, and cooperative driving maneuvers. This paper delves into the technical aspects, challenges, and potential applications of LiFi-enabled V2V communication, shedding light on its promising role in revolutionizing the future of smart and connected transportation systems

Digital Twin-Enabled Efficient Federated Learning for Collision Warning in Intelligent Driving

Digital Twin-Enabled Efficient Federated Learning for Collision Warning in Intelligent Driving

Are front crash prevention systems less effective at preventing rear-end crashes where trucks and motorcycles are struck?

Objective Automatic emergency braking (AEB) and forward collision warning (FCW) are effective at preventing rear-end crashes, but they may perform better in some rear-end crash scenarios than others. The goal of this study was to estimate the effects of front crash prevention systems equipped to passenger vehicles in crashes where another passenger vehicle, a medium/heavy truck, or a motorcycle is struck and compare effectiveness by struck vehicle type. Methods More than 160,000 two-vehicle rear-end crashes were identified where a passenger vehicle with or without FCW and AEB was the striking vehicle and another passenger vehicle, medium/heavy truck, or motorcycle was the struck vehicle. Poisson regression was used to estimate the effect of front crash prevention by struck vehicle type on rear-end crash rates per registered vehicle year, accounting for the state and year of the crash and the make, model year, class, and engine type of the striking vehicle. Results Front crash prevention was associated with a 53% reduction in rear-end crash rates when striking another passenger vehicle, which was significantly larger than the reductions of 38% when striking a medium/heavy truck and 41% when striking a motorcycle. Reductions in rear-end injury crash rates when striking a passenger vehicle also were larger than when striking a medium/heavy truck and when striking a motorcycle. Discussion If all passenger vehicles were equipped with FCW and AEB that were as effective in crashes striking a truck or motorcycle as they are in crashes with another passenger vehicle, over 5,500 additional crashes with medium/heavy trucks and 500 with motorcycles could potentially be prevented annually in the United States above what would be expected from current front crash prevention systems. Extending front crash prevention testing in consumer information programs to include motorcycle and truck targets could encourage auto manufacturers to improve performance in these crash scenarios.

Research on collision warning strategy considering characteristics of drivers and road conditions

Collision warning strategy considering different characteristics of drivers and complex road conditions is of great importance for active safety control of autonomous vehicles. However,existing estimation methods for Tire-road friction coefficient (TRFC) cannot deal with the problem of estimation accuracy degradation caused by state mutation. Therefore, an adaptive cascaded strong tracking unscented Kalman filter (ACSTUKF) is proposed to estimate TRFC. The proposed algorithm consists of two strong tracking filters, which are connected in series with each other. Fuzzy control is introduced to estimate the reaction time of different drivers. Simulation experiments and real vehicle tests are carried out to demonstrate the effectiveness of the proposed strategy. Results show that the proposed ACSTUKF has a more excellent performance for TRFC than EKF and UKF, which greatly reduces the impact of state mutation. Moreover, ACSTUKF shows a strong robustness to driving conditions and road conditions. The established collision warning strategy is consistent with the actual situation, which realizes the adaptation of human and road.

Advanced Driver-Assistance Systems: Features Journey for Tomorrow

Intelligent associated vehicles (ICVs) are accepted to completely change people sooner rather than later by making the transportation more secure, cleaner and more agreeable. Albeit numerous models of ICVs have been created to demonstrate the idea of independent driving and the plausibility of further developing traffic effectiveness, there actually exists a critical hole prior to accomplishing large scale manufacturing of undeniable level ICVs. The goal of this study is to introduce an outline of both the cutting edge and future viewpoints of key necessary advances for future ICVs. It is a moving undertaking to survey every connected work and foresee their future viewpoints, particularly for such a perplexing and interdisciplinary area of examination. Advanced driver-assistance systems (ADASs) have become a salient feature for safety in modern vehicles. They are also a key underlying technology in emerging autonomous vehicles. State-of-the-art ADASs are primarily vision based, various type of features for ex. Lane Departure Warning (LDW), Blind Spot Monitoring (BSM), Forward Collision Warning (FCW), Automatic Emergency Braking (AEB), Traffic Sign Recognition, High Beam Assist, Rear Cross Traffic Alert, Driver Drowsiness Detection, Obstacle Aware Acceleration, Auto-steer etc. The paper aims at giving a complete picture focusing on the ADAS features for the user-friendly design of human-machine interfaces between driver and assistance system.

Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication

Pedestrians, bicyclists, and scooterists are Vulnerable Road Users (VRUs) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S., even though road vehicles now have perception sensors like cameras to detect risk and issue collision warnings or apply emergency braking. Perception sensors like cameras are highly affected by lighting and weather conditions. Cameras, radar, and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs on trajectories with a potential collision risk. An Android smartphone app with low-energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data are smoothed using a Kalman filter, and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU, and issues driver warnings with different severity levels. The warning severity level is based on the analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision-mitigation system.

A Virtual Vehicle–Based Car‐Following Model to Reproduce Hazmat Truck Drivers’ Differential Behaviors

Enhancing hazmat truck safety through advanced driving assistance systems (ADAS) relies on both system efficacy and driver reactions. This study investigates the driving behaviors of hazmat truck drivers in response to forward collision warnings (FCWs). Traditional warning triggering methods struggle to capture diverse and immediate driver responses; therefore, our research employs a vision‐based framework for driving data extraction and utilizes the K‐means++ clustering method for response‐based classification. Moreover, we propose an enhanced version of the intelligent driver model (IDM) based on the concept of a virtual vehicle to reproduce hazmat truck drivers’ differential behaviors during risky car‐following periods, achieving results that depict improved driving simulations. This model is compared with classic benchmarks, including the IDM, optimal velocity model (OVM), and full velocity difference (FVD) model, demonstrating superior performance in terms of traffic stability and safety in extreme scenarios. Our findings highlight that preaction drivers tend to accelerate before receiving warnings, opting to overtake rather than maintain safe distances. In contrast, calm drivers decelerate in anticipation of the warning, showcasing their awareness of maintaining safety. The analysis reveals that aggressive drivers are predominantly in the 41–45 age group, indicating a higher skill level, while calm drivers are more commonly older, reflecting a trend in cautious driving behaviors. Overall, our research contributes to the development of effective ADAS by considering real‐time driver responses and emphasizes the potential of our model to revolutionize commercial ADAS adoption and enhance road safety for hazmat operations.

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conservatism, potentially resulting in false–positive risk events in complicated real-world applications. In this paper, we combine two reachability analysis techniques, a backward reachable set (BRS) and a stochastic forward reachable set (FRS), and propose an integrated probabilistic collision–detection framework for highway driving. Within this framework, we can first use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step. Thus, the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data. The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising for real-world applications.

Research of the New Generation Marine Navigation Security Communication System

With the number of Chinese ships rapidly increasing, the available channels for the Automatic Identification System (AIS) are serious congested. This has led to a sharp rise in collision warnings. The maritime communication System is comprehensively updated from AIS to VHF Data Exchange System (VDES) in order to ensure efficient communication of huge scale, high density maritime ships as well as guarantee marine navigation security. In this paper, the new generation marine navigation security communication system based on VDES is studied. Designing communication systems for ships at sea, shore-based base stations and space-based satellites; The two-way data exchange between huge scale, high density ships-to-ships and ship-to-shore by VHF frequency band in nearshore and port areas can promote the development of maritime navigation technology and maritime economic.

Investigating the impact of influential factors on crash types for autonomous vehicles at intersections

Autonomous Vehicles (AVs) are being promoted as an emerging technology with the potential to improve traffic efficiency and safety. However, the scarcity of publicly available AV crash data contributes to a limited understanding of safety issues for deploying AVs. To gain a deeper understanding of the factors contributing to AV crashes, this study created a unique dataset by combining 445 intersection AV crashes from the California Department of Motor Vehicles with detailed roadway geometric and traffic characteristic data. To address the issue of imbalanced data, the Borderline synthetic minority oversampling technique (BL-SMOTE) was utilized in this study. A framework for analyzing AV crash types at intersections was also developed using the Categorical gradient boosting (Catboost) and Bayesian networks (BNs) models. The results show that inappropriate vehicle maneuvers, such as sudden lane changes or speed control, can increase the likelihood of an AV being involved in a sideswipe crash. Additionally, in scenarios where the AADT of the major road is lower, AVs are more prone to rear-end crashes when proceeding. These findings can provide valuable insights for creating safety management strategies in different situations and improve the development of innovative car warning technologies, such as rear-end collision warnings.

Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle

Motorcycle detection and collision warning are essential features in advanced driver assistance systems (ADAS) to ensure road safety, especially in emergency situations. However, detecting motorcycles from videos captured from a car is challenging due to the varying shapes and appearances of motorcycles. In this paper, we propose an integrated and innovative remote sensing and artificial intelligence (AI) methodology for motorcycle detection and distance estimation based on visual data from a single camera installed in the back of a vehicle. Firstly, MD-TinyYOLOv4 is used for detecting motorcycles, refining the neural network through SPP (spatial pyramid pooling) feature extraction, Mish activation function, data augmentation techniques, and optimized anchor boxes for training. The proposed algorithm outperforms eight existing YOLO versions, achieving a precision of 81% at a speed of 240 fps. Secondly, a refined disparity map of each motorcycle’s bounding box is estimated by training a Monodepth2 with a bilateral filter for distance estimation. The proposed fusion model (motorcycle’s detection and distance from vehicle) is evaluated with depth stereo camera measurements, and the results show that 89% of warning scenes are correctly detected, with an alarm notification time of 0.022 s for each image. Outcomes indicate that the proposed integrated methodology provides an effective solution for ADAS, with promising results for real-world applications, and can be suitable for running on mobility services or embedded computing boards instead of the super expensive and powerful systems used in some high-tech unmanned vehicles.

Digital twin-enabled collision early warning system for marine piling: Application to a wharf project in China

The safety and effectiveness of marine piling construction are hindered by the display of imprecise spatial information and the potential for collisions due to the complexity of pile positions and attitudes. Digital twin (DT) can solve the above problems in piling construction because of their inherent three-dimensionality and real-time feedback. The present study proposes a system that utilizes the DT framework to construct a collision early warning system intended for marine piling. A five-dimensional model is used to introduce the overall architecture. The main twin objects are piles. To maximize the effectiveness of DT, the five-dimensional model is composed of four independently maintainable development modules. Consequently, a prototype of the system is created. The core of the system comprises an algorithm for pile positioning and an algorithm for collision early warning, especially for complex pile groups. Two distinct implementations are identified: one involves controlling the virtual with the real, while the other involves controlling the real with the virtual. Field experiments are conducted with the aim of establishing a benchmark for collision early warning during the construction process. The prototype of the system is capable of producing a simulated environment through a dual-layer mapping approach, which involves pile-boat mapping and early-warning feedback mapping. The presented system in a practical wharf project showcases both the capacity for three-dimensional visualization of collision warning and the ability to provide feedback for boat control. The research enables more intuitive and effective pile positioning.

Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion

Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these issues, this paper proposes a decision-level fusion collision warning strategy. The vision algorithm and radar tracking algorithm are improved in order to reduce the false alarm rate and omission rate of forward collision warning. Firstly, this paper proposes an information entropy-based memory index for an adaptive Kalman filter for radar target tracking that can adaptively adjust the noise model in a variety of complex environments. Then, for visual detection, the YOLOv5s model is enhanced in conjunction with the SKBAM (Selective Kernel and Bottleneck Attention Mechanism) designed in this paper to improve the accuracy of vehicle target detection. Finally, a decision-level fusion warning fusion strategy for millimeter-wave radar and vision fusion is proposed. The strategy effectively fuses the detection results of radar and vision and employs a minimum safe distance model to determine the potential danger ahead. Experiments are conducted under various weather and road conditions, and the experimental results show that the proposed algorithm reduces the false alarm rate by 11.619% and the missed alarm rate by 15.672% compared with the traditional algorithm.

V2XCom: Lightweight and secure message dissemination scheme for Internet of vehicles

AbstractSmart cities provide a sustainable transport ecosystem to connect smart vehicles through sensors and networking units. Internet‐of‐vehicles (IoV) is vital in disseminating various messages, including road safety, exact location sharing, road accidents and blocks, collision warning, driver assistance, network congestion, or toll payment among vehicle‐to‐anything (V2X) units. Due to the mission‐critical nature of the IoV ecosystem, it requires reliable, lightweight, and real‐time communication for vehicle‐to‐vehicle (V2V) and V2X units. However, due to the availability of insecure wireless channels, an adversary can perform several security attacks such as replay, password guessing, masquerade, trace, message tampering, Man‐in‐the‐middle attack (MIMA), and plain‐text attacks in an IoV environment which may lead to potential disruptions. Motivated by the aforementioned facts, we propose a V2XCom, a lightweight and secure message dissemination scheme for the IoV network using low‐cost cryptographic SHA‐256, XoR operation, and concatenation. We performed security verification of V2XCom using the Scyther and AVISPA tools. Moreover, security proofs are provided for an informal security analysis of the proposed scheme. We have done a comparative analysis of a V2XCom with recent dissemination schemes in the IoV ecosystem concerning security features, communication latency, computational cost, and energy usage.

Deep neural network for non-cooperative space target intention recognition

Given the critical importance of intention recognition for non-cooperative space targets in enhancing spatial situational awareness and mitigating threats, this paper proposes a fast and accurate method based on a deep neural network for identifying the intentions. The proposed method takes the instantaneous single relative motion position and velocity of the non-cooperative target as input and calculates the probability of different potential intentions in real-time. To this end, the possible geometric configurations of relative motions of the targets are characterized and classified based on the Clohessy-Wiltshire (CW) equation, and intentions with respect to different types of motion are defined and grouped accordingly. To ensure the method's applicability for large range scenario, the required dataset in two-body dynamics is generated using satellite orbit extrapolation techniques. The deep neural network is trained on the dataset using back-propagation, and experiments are designed to evaluate the method's performance and the recognition accuracy reached 0.9289. The proposed method outperforms the dynamical characteristics matching-based method in terms of recognition accuracy, range of applicability, and time efficiency. The method has potential applications in spacecraft collision warning and orbital pursuit-evasion games, where rapid and accurate identification of non-cooperative target intentions is critical.

Developing a new integrated advanced driver assistance system in a connected vehicle environment

Advanced driver assistance systems (ADASs) can effectively enhance driving and safety performance. Due to the inherent limitations of in-vehicle technologies concerning information sharing, existing studies mainly focus on demonstrating the effectiveness of onboard sensor-based individual ADAS functions rather than their collaborative effectiveness. Thanks to the emerging connected vehicle (CV) technologies, it is viable to physically realize the collaboration and coordination between different ADAS functions. This study aims to synthesize seven ADAS functions into an integrated advanced driving assistance system (iADAS) in a CV environment. The seven ADAS functions include omni-directional collision warning, lane-change warning, curve speed warning, emergency event notification, car-following guidance, identification of variable speed limits, and information services. The activation indicators and activation conditions of these ADAS functions are derived based on a single local coordinate system. This derivation has considered the real-time motion states of the ego and surrounding vehicles. Different ADAS functions are classified based on their roles in accident reduction, traffic efficiency enhancement, and driver convenience improvement. Afterwards, their priority of releasing information is set by considering the classification and primary functionality. Finally, the effectiveness of the iADAS is validated in field tests. Testing results reveal that the iADAS helps reduce rear-end collisions and prevent rollovers or sideslips on curved roads. Furthermore, younger drivers respond faster with higher driving stability regarding lateral collision warnings. Young, well-educated, and low-risk taking drivers maintain a short but safe time headway to the leading vehicle.

Digital Forensic Analysis of Vehicular Video Sensors: Dashcams as a Case.

Dashcams are considered video sensors, and the number of dashcams installed in vehicles is increasing. Native dashcam video players can be used to view evidence during investigations, but these players are not accepted in court and cannot be used to extract metadata. Digital forensic tools, such as FTK, Autopsy and Encase, are specifically designed for functions and scripts and do not perform well in extracting metadata. Therefore, this paper proposes a dashcam forensics framework for extracting evidential text including time, date, speed, GPS coordinates and speed units using accurate optical character recognition methods. The framework also transcribes evidential speech related to lane departure and collision warning for enabling automatic analysis. The proposed framework associates the spatial and temporal evidential data with a map, enabling investigators to review the evidence along the vehicle's trip. The framework was evaluated using real-life videos, and different optical character recognition (OCR) methods and speech-to-text conversion methods were tested. This paper identifies that Tesseract is the most accurate OCR method that can be used to extract text from dashcam videos. Also, the Google speech-to-text API is the most accurate, while Mozilla's DeepSpeech is more acceptable because it works offline. The framework was compared with other digital forensic tools, such as Belkasoft, and the framework was found to be more effective as it allows automatic analysis of dashcam evidence and generates digital forensic reports associated with a map displaying the evidence along the trip.

Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually Impaired.

This paper presents a system that utilizes vision transformers and multimodal feedback modules to facilitate navigation and collision avoidance for the visually impaired. By implementing vision transformers, the system achieves accurate object detection, enabling the real-time identification of objects in front of the user. Semantic segmentation and the algorithms developed in this work provide a means to generate a trajectory vector of all identified objects from the vision transformer and to detect objects that are likely to intersect with the user's walking path. Audio and vibrotactile feedback modules are integrated to convey collision warning through multimodal feedback. The dataset used to create the model was captured from both indoor and outdoor settings under different weather conditions at different times across multiple days, resulting in 27,867 photos consisting of 24 different classes. Classification results showed good performance (95% accuracy), supporting the efficacy and reliability of the proposed model. The design and control methods of the multimodal feedback modules for collision warning are also presented, while the experimental validation concerning their usability and efficiency stands as an upcoming endeavor. The demonstrated performance of the vision transformer and the presented algorithms in conjunction with the multimodal feedback modules show promising prospects of its feasibility and applicability for the navigation assistance of individuals with vision impairment.