A Rear-End Collision Risk Evaluation and Control Scheme Using a Bayesian Network Model

Evaluation of collision risk plays a key role in developing the expert system of navigation and collision avoidance. This paper presents a new collision risk model formula that is one modification model on the basis of one approach to the evaluation of collision risk using sech function produced in earlier studies. And as a tool of the evaluation field of ship collision, this paper applied the new model in appraising the collision risk and represented how to decide the safe range of own ship's action. Moreover this paper also analyzed theoretically how to determine the coefficients as described in the new modification model, and suggested the appropriate values as applicable.

Radars are already used for collision avoidance during more than 50 years. During this period many proposal for collision avoidance by using radar have been made, and the experience of their use have been gained. However the effectiveness of these proposals for collision avoidance is not enough, especially in congested water. The Automatic Identification System (AIS) will be equipped all ships of 300 gross tonnage and upwards engaged on international voyages. The AIS is expected to be very useful for collision avoidance. Even if AIS is used, it is necessary to consider the effect of predicted error of ship's position on the evaluation of collision risk. In this paper an approach to improve the evaluation and expression of collision risk is shown. This method is able to show the obstacle zone by target (OZT), considered an estimated error of ship's speed, and it will be useful for collision avoidance in congested water. Expression of OZT shown in this paper will be easy to trace the origin target of OZT and to check the fault information transmitted by the target.

One of the key challenges that the Advanced Air Mobility (AAM) sector faces is airspace segregation, which represents a conservative approach to minimize the risk of mid-air collisions between unmanned and manned aircraft. UAS must have the capabilities to identify, detect and resolve any possible conflicts or collision threats. Sense-and-avoid (SAA) algorithms are responsible for separation assurance and collision avoidance. SAA systems are becoming a requirement for UAS operations. In this paper, we extend the concept of collision avoidance to not only include avoiding a possible impact but also evading possible encounters of wake turbulence from intruder aircraft. This novel approach can greatly increase the capability of SAA by minimizing not only the risk of collision but also the loss of control due to any wake vortices. Moreover, a model for calculating the safe separation distance between different combinations of aircraft based on wake vortices intensity and decay is presented. Real-life effects such as wind turbulence are analyzed to study the effect on the effectiveness of the SAA system. In this paper, we focus on generating avoidance volumes that encapsulate both the hazardous wake and the uncertainty arising from the stochastic nature of wind turbulence. This is simulated as an encounter between two Manned/Unmanned Aerial Vehicles, which resembles a typical interaction scenario in the AAM field.

Cut-in situations occurs when a vehicle intentionally changes lane and ends up in front of another vehicle or in-between two vehicles. In such situations, having a method to indicate the collision risk prior to making the cut-in maneuver could potentially reduce the number of sideswipe and rear end collisions caused by the cut-in maneuvers. This paper propose a new risk indicator, namely cut-in risk indicator (CRI), as a way to indicate and potentially foresee collision risks in cut-in situations. As an example use case, we applied CRI on data from a driving simulation experiment involving a manually driven vehicle and an automated platoon in a highway merging situation. We then compared the results with time-to-collision (TTC), and suggest that CRI could correctly indicate collision risks in a more effective way. CRI can be computed on all vehicles involved in the cut-in situations, not only for the vehicle that is cutting in. Making it possible for other vehicles to estimate the collision risk, for example if a cut-in from another vehicle occurs, the surrounding vehicles could be warned and have the possibility to react in order to potentially avoid or mitigate accidents.

In recent year, research on autonomous ships has progressed, and collision avoidance is one of the important tasks among them. Collision avoidance maneuvering includes judgment on uncertain events such as forecast of motions of other vessels. Therefore, it is effective to improve safety by navigating away from areas where a complicated encounter, in other words high collision risk, is likely to occur even when establishing the collision avoidance technology. Collisions tend to occur repeatedly in similar places. For this reason, areas where complicated encounter situations are likely to occur are considered to be areas where collisions occur frequently in the past. In addition, it is considered that the area in which many abnormal approaches of the ship occur is also an area where complicated encounter is likely to occur. From the analysis of AIS data, the ship close point is extracted and shown on the map. Also, in order to clarify the collision risk in the sea area, collision points and ship close points were converted into continuous distribution by kernel density estimation method and shown. Then, considering the time, the kernel density estimation method was extended to three dimensions so that the collision risk of the target area can be estimated. We used this to estimate the collision risk along the planned route. It was found that even on the same route the collision risk greatly differs depending on the time zone to be sailed. Even in existing vessels, the collision risk on the planned route can be obtained using this system. Therefore, it is possible to expect the accident prevention effect by allowing the operator to navigate by grasping the high-risk sea area.

Various collision avoidance systems (CASs) have been developed and employed in human-operated vehicles as well as more recently in autonomous vehicles. Most of the existing CASs perform an override function to actuate automatic emergency braking in a critical situation based on the current traffic information obtained from in-vehicle sensors or short-range vehicular communications. These CASs focus on the critical situation in the vicinity of the subject vehicle, which means they may have negative influences on the subject vehicle and its following vehicles, particularly when the leader vehicle of a platoon with short headway applies harsh braking to mitigate an impending collision risk. This study proposes a risk predictive CAS (RPCAS) which executes predictive deceleration with mild braking in advance to prevent a potential rear-end collision by predicting the collision risk arising from a downstream site. To evaluate the performance of the RPCAS, the proposed system is compared with several existing CASs in various car-following cases based on a microscopic traffic simulation. The simulation results show that the RPCAS can effectively reduce the rear-end collision risk with less harsh braking compared with the existing CASs. Furthermore, the RPCAS enables vehicles arriving from upstream to anticipate a potential crash, which provides them with sufficient time to reduce their current speeds proactively. The research findings suggest that the proposed system can attenuate the negative impacts of the previous CASs in relation to traffic and vehicular safety.

ABSTRACTThis paper proposes a new safety system named emergency steering assist (ESA), which consists of the autonomous steering subsystem and differential braking subsystem. The control system is developed to mediate the conflict objectives of vehicle stabilisation and rear-end collision avoidance in highway driving. Instead of predefining a collision-free trajectory in the entire process of collision avoidance, the process of collision avoidance is divided into two stages. In stage 1, where the vehicle is guided to enter the adjacent lane with a constant centripetal acceleration to avoid collision, the steering manoeuvre is determined by the feedforward controller based on the steering dynamics. In stage 2, where the vehicle is guided along the centreline of the adjacent lane, the steering manoeuvre is determined by the controller based on the theory of model predictive control. In addition, the differential braking subsystem is designed with a comprehensive treatment to the requirements of collision avoidance and vehicle stabilisation. Finally, the simulation results demonstrate that the proposed ESA system can effectively achieve better balance between an emergency collision avoidance manoeuvre and vehicle stabilisation at high speeds in different conditions. The hardware-in-the-loop experiment developed by the authors is used to validate the real-time performance and effectiveness of the proposed control scheme.

Collision risk assessment and automatic obstacle avoidance strategy for teleoperation robots

More than 23% of annual vehicle accidents are rear-end collisions, this gives an important test-case for enhanced collision avoidance approaches based on v2v wireless communications. V2V wireless network concern with the impact of a IEEE 802.11 based multi-hop MAC protocol that propagates an emergency warning message (EWM) down a platoon of cars on a highway. The design objective is to ensure reception of this message with stringent delay constraints so as to provide drivers with requisite available manoeuvre time (AMT) to avoid rear-end collision. This V2V wireless communication protocol provides a realistic simulation in Ns-2 for various topologies (lane-1& lane-3) and the performances were evaluated for different parameters such as EWM delay, delay due to overhead packets and probability of successful delivery of the emergency warning message (EWM).

In the conditions of bad weather, the small vessels maneuvering ability of collision avoidance is seriously reduced and the vulnerability to maritime accidents increases due to the influence of strong wind and wave. This paper proposes a collision avoidance decision-making and control method for small vessels using the integrated collision risk assessment based on fuzzy logic. First, the speed of wind and height of wave in the bad weather conditions are fuzzified. Then, the collision risk indices are selected for fuzzy inference to get the basic collision risk which will be integrated with the vulnerability to collision accidents and it helps to improve the accuracy of collision risk assessment. According to the current multi-ships encounter situations, the similar cases are retrieved from the case database, and the collision avoidance decision is given after fuzzy adjustments. To implement collision avoidance actions, a fuzzy algorithm for nonlinear ship heading dynamic surface control (DSC) control considering input saturation and uncertainties is designed. The problem of "computational expansion" that may occur in high-order systems is avoided by using DSC technology. Finally, simulation experiments are implemented to verify the effectiveness of the collision avoidance method.

Stroke-associated pneumonia (SAP) is a major cause of mortality following ischemic stroke (IS). However, existing predictive models for SAP often lack transparency and interpretability, limiting their clinical utility. This study aims to develop an interpretable Bayesian network (BN) model for predicting SAP in IS patients, focusing on enhancing both predictive accuracy and clinical interpretability. This retrospective study included patients diagnosed with IS and admitted to the Second Affiliated Hospital of Nanchang University between January and December 2019. Clinical data collected within 48 h of admission and SAP occurrences within 7 days were analyzed. Dimensionality reduction was performed using Least Absolute Shrinkage and Selection Operator regression, while data imbalances were addressed using synthetic minority oversampling technique. A BN model was trained using a hill-climbing algorithm and compared to logistic regression, decision trees, deep neural networks, and existing risk-scoring systems. Decision curve analysis was used to assess clinical usefulness. Of the 1252 patients, 165 (13.18%) patients had SAP within 7 days of admission. The BN model identified age, risk of pressure injury (PI), National Institutes of Health Stroke Scale (NIHSS) score, and C-reactive protein (CRP) as significant prognostic factors. The BN model achieved an area under the curve of 0.85(95% CI: 0.78-0.92) on the test set, outperforming other models and demonstrating a greater net benefit in clinical decision-making. Age, risk of PI, NIHSS score, and CRP are significant predictors of SAP in IS patients. The interpretable BN model demonstrates superior predictive performance and interpretability, suggesting its potential as an effective and interpretable tool for clinical decision support in SAP risk assessment.

Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate.

One of the possible uses of short-distance car-to-car wireless communication is for broadcasting brake motivation. An equation for calculating delay time for safe breaking between vehicles is developed. Delay time is used as the criteria for evaluating the inter-vehicle communication effect on rear-end collision avoidance. Simulation results show that: with high communication percentage and wide communication range, the total number of collisions in the whole system will drop significantly.

Unsafe rear-end collision avoidance in Alzheimer's disease

Development of rear-end collision avoidance system