Evasive Maneuvers Research Articles

The Role of the Prefrontal Cortex and Functional Connectivity during Maritime Operations: An fNIRS study.

IntroductionWatchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision‐making can increase the likelihood of a collision.MethodsA study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20‐min period of sustained attention to locate a target vessel and (2) a 10‐min period of decision‐making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15‐channel functional near‐infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph‐theoretic measures.ResultsNeurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision‐making. The graph‐theoretic analysis revealed that density declined during decision‐making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver.ConclusionsThe right lateral area of the PFC is sensitive to watchkeeping and decision‐making during operational performance. Graph‐theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety‐critical performance.

Lateral Control of an Autonomous and Connected Following Vehicle With Limited Preview Information

Lateral control of an autonomous and connected vehicle (ACV), especially in emergency situations, is important from the safety viewpoint. In these situations, the trajectory to be followed by an ACV must either be planned in real-time (e.g., for a possible evasion maneuver if the obstacle to be avoided is detected) or be communicated from its preceding vehicle. Typically, the trajectory information is available to the following ACV in the form of GPS time samples. From the viewpoint of lateral control, the lateral velocity information is not readily available and the feedback structure must reflect this reality. In this paper, we develop a methodology to synthesize a lateral control algorithm for a following ACV in a two-vehicle platoon in two steps: 1) From the limited preview information of the trajectory to be tracked via samples of GPS way points, we estimate the radius of curvature of the trajectory using “least-square” estimation and 2) develop a fixed-structure feedback control scheme for following the predecessor by synthesizing the set of stabilizing gains corresponding to lateral position error, heading error and heading rate error. Numerical simulation and experimental results corroborate the effectiveness of the proposed schemes.

Integrated nonlinear model predictive control for automated driving

This work presents a Nonlinear Model Predictive Control (NMPC) scheme to perform evasive maneuvers and avoid rear-end collisions. Rear-end collisions are among the most common road fatalities. To reduce the risk of collision, it is necessary for the controller to react as quickly as possible and exploit the full vehicle maneuverability (i.e., combined control of longitudinal and lateral dynamics). The proposed design relies on the simultaneous use of steering and braking actions to track the desired reference path and avoid collisions with the preceding vehicle. A planar vehicle model was used to describe the vehicle dynamics. In addition, the dynamics of the brake system were included in the NMPC prediction model. Furthermore, the controller incorporates constraints to ensure vehicle stability and account for actuator limitations. In this respect, the constraints were defined on Kamm circle and Ideal Brake Torque Distribution (IBD) logic for optimal tire force and brake torque distribution. To evaluate the design, the performance of the proposed NMPC was compared with two ”more classical” MPC designs that rely on: (i) a linear bicycle model, and (ii) a nonlinear bicycle model. The performance of these three controller designs was evaluated in simulation (using a high-fidelity vehicle simulator) via relevant KPIs, such as reference tracking Root Mean Square (RMS) error, controller’s rise/settling time, and Distance to Collision (i.e., the lateral distance by which collision was avoided safely). Different single-lane-change maneuvers were tested and the behavior of the controllers was evaluated in the presence of lateral wind disturbances, road friction variation, and maneuver aggressiveness.

The type and variation of evasive manoeuvres during an attacking task differ across a rugby league development pathway

ABSTRACT This study examined the relationship between evasive skill and developmental level in a rugby league (RL) talent pathway. An observational and cross-sectional research design was used with a total sample of 90 male participants (under 18, n = 30; under 20, n = 30; and state league, n = 30) performing an attack play task, requiring three attackers to compete against two defenders to successfully generate a “line-break”. Assessment criteria of the task included start position, type of evasive manoeuvre and task outcome (successful line-break or not), with relationships determined using the Fischer’s exact test (Crosstabs Command) with adjusted residuals (AR) and the multinomial logistic regression. Outcome scores for the task did not significantly differ between development levels, but the relationship between development level and evasive manoeuvres was significant (χ2 = 35.916; df = 26; P = 0.026; ES = 0.27). State league players had a greater frequency of “angled run”, “all square run” and combinations of evasive manoeuvres compared to the other levels. This study demonstrated that variation and type of evasive manoeuvre used by players differed across developmental level. These results could support the design of training activities intended to develop evasive skill in talented junior RL players.

Combining radiotherapy and immunotherapy in definitive treatment of head and neck squamous cell carcinoma: review of current clinical trials.

BackgroundHead and neck squamous cell carcinoma (HNSCC) presents as locally advanced disease in a majority of patients and is prone to relapse despite aggressive treatment. Since immune checkpoint inhibitors (ICI) have shown clinically significant efficacy in patients with recurrent/metastatic HNSCC (R/M HNSCC), a plethora of trials are investigating their role in earlier stages of disease. At the same time, preclinical data showed the synergistic role of concurrently administered radiotherapy and ICIs (immunoradiotherapy) and explained several mechanisms behind it. Therefore, this approach is prospectively tested in a neoadjuvant, definitive, or adjuvant setting in non-R/M HNSCC patients. Due to the intricate relationship between host, immunotherapy, chemotherapy, and radiotherapy, each of these approaches has its advantages and disadvantages. In this narrative review we present the biological background of immunoradiotherapy, as well as a rationale for, and possible flaws of, each treatment approach, and provide readers with a critical summary of completed and ongoing trials.ConclusionsWhile immunotherapy with ICIs has already become a standard part of treatment in patients with R/M HNSCC, its efficacy in a non-R/M HNSCC setting is still the subject of extensive clinical testing. Irradiation can overcome some of the cancer’s immune evasive manoeuvres and can lead to a synergistic effect with ICIs, with possible additional benefits of concurrent platinum-based chemotherapy. However, the efficacy of this combination is not robust and details in trial design and treatment delivery seem to be of unprecedented importance.

Implications of Crashes Involving Roadway Objects for Machine Vision-Based Driving Systems

Crashes involving roadway objects can cause severe injuries and property damage. Utilizing data from the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study (NDS), this study investigated crashes involving roadway objects and their implications for the potential of machine vision-based driving systems in preventing such crashes. A comprehensive statistical and machine learning analysis was first conducted to identify major factors affecting the occurrence and severity of such events. Machine vision performance metrics (based on the SHRP 2 NDS cameras) and human driving decisions were then analyzed to identify opportunities where machine vision systems could particularly mitigate risk factors. The results suggest that driver behaviors/errors, speed, reaction time, and object characteristics played the most significant role in the occurrence and severity outcome of the SHRP 2 events. The average object detection distance based on the SHRP 2 cameras was approximately 20 m for all objects. The average reaction time provided by the SHRP 2 cameras was 1.5 s for all events but 1.1 s for events involving animals and roadway debris. In general, the machine vision reaction time was longer than the driver reaction time for approximately 95% of all analyzed events and 75% of the events in which drivers reacted before collisions. Drivers were able to make safe evasive maneuvers in 56% of all analyzed events and 72% events involving roadway debris and animals. Based on these results, the paper discusses in detail when and how machine vision systems could assist in preventing crashes involving roadway objects.

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler

This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system.

Hit-to-kill accurate minimum time continuous second-order sliding mode guidance for worst-case target maneuvers

The recent research is focused on the development of an advanced interceptor missile that has hit-to-kill accuracy against ballistic targets performing evasive maneuvers. In this paper, a guidance law that achieves hit-to-kill accuracy against ballistic target executing worst-case maneuvers is developed using second-order sliding mode control and optimal control. The guidance law thus developed is continuous and has minimum time convergence for worst-case target maneuvers. The performance of the continuous guidance law with minimum time convergence is evaluated through numerical simulations against ballistic targets executing step maneuvers with changing polarity and weaving maneuvers.

Application of Machine learning Algorithms in Autonomous Vehicles Navigation System

Autonomous vehicles are the future of transportation and it also expected to become a fully-fledged reality within a decade. The major giants in the automotive industry like Toyota with Microsoft and Amazon, Mercedes with Bosch, Audi with Huawei, etc. are hard pressing their transition from conventional vehicle to autonomous vehicles. The state of Karnataka, for instance, had approximately 205,200 registered taxis higher than Madhya Pradesh 174,900 registered cabs from 2014 to 2015. This results in higher traffic congestion, pollution and high fuel consumption due to unorganized driving practice. As a consequence, this presents a great deal of opportunities for autonomous cars as they can significantly reduce the accident rate and improve effective and stress free parking, optimal running time, fuel economy etc. It also reduces traffic congestion as all the autonomous cars can be synchronized together through cloud. But the crucial part is their navigation system. Although a number of sophisticated forms of technology like such as radars, lasers, and high definition cameras for mapping, localization, obstacle detection, etc, are used, autonomous vehicles are still trying to reach perfection to navigate precisely under uninformed terrains and dynamics obstacles on the way like pedestrians, erratic drivers, traffic and climatic conditions. Events like heavy snow and rain, improper lane marking makes it hard to detect an obstacle on the road for evasive maneuver within limited response time period. Machine learning is one of the fast growing technologies that provide optimal solution to overcome the challenges in the autonomous navigation system. This paper focuses on the application of different machine learning algorithms and compares the use, challenge and scope of the same. The paper also focuses on the future direction of machine learning with reference to autonomous navigation system.

Nonaggressive behavior: A strategy employed by an obligate nest invader to avoid conflict with its host species.

In addition to its builders, termite nests are known to house a variety of secondary opportunistic termite species so‐called inquilines, but little is known about the mechanisms governing the maintenance of these symbioses. In a single nest, host and inquiline colonies are likely to engage in conflict due to nestmate discrimination, and an intriguing question is how both species cope with each other in the long term. Evasive behaviour has been suggested as one of the mechanisms reducing the frequency of host‐inquiline encounters, yet, the confinement imposed by the nests' physical boundaries suggests that cohabiting species would eventually come across each other. Under these circumstances, it is plausible that inquilines would be required to behave accordingly to secure their housing. Here, we show that once inevitably exposed to hosts individuals, inquilines exhibit nonthreatening behaviours, displaying hence a less threatening profile and preventing conflict escalation with their hosts. By exploring the behavioural dynamics of the encounter between both cohabitants, we find empirical evidence for a lack of aggressiveness by inquilines towards their hosts. Such a nonaggressive behaviour, somewhat uncommon among termites, is characterised by evasive manoeuvres that include reversing direction, bypassing and a defensive mechanism using defecation to repel the host. The behavioural adaptations we describe may play an important role in the stability of cohabitations between host and inquiline termite species: by preventing conflict escalation, inquilines may improve considerably their chances of establishing a stable cohabitation with their hosts.

Don’t Lose Your Heart to a Car. Extracorporeal Ejection of the Heart by Forceful Upward Thoraco-abdominal Blunt Trauma in Human and Animal.

The traumatic extracorporeal ejection of a beating heart has not been witnessed and reported in the literature previously, to our knowledge. Here we present two cases of vehicle accidents leading to this fatal injury, both in a rabbit and a human.
 Case 1. One late evening in June, in the land of the midnight sun, a young man was driving to his favourite fishing river for a spot of trout fishing in a remote part of Eastern Finland. While driving his aged Nissan Almera at 60 km/h down a dusty gravel road on this dusky night, suddenly a young male rabbit jumped onto the road and appeared in the beam of his headlights. Despite the man’s evasive manoeuvres, the rabbit was struck by the left front corner of the car. After hitting the brakes and the car eventually coming to a standstill, the young man went to inspect the animal he had hit….
 In conclusion, we present that in a very rare constellation of forceful blunt trauma to the chest, the heart can be completely avulsed and ejected from the body in human as in animals. Larger prospective and randomised studies are probably not needed to confirm these findings. Just be careful – it is possible to lose your heart to a car.

Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots. • This study showed how driver compensates differently according to weather conditions to avoid crash event. • This study provided a discrimination threshold between normal and risky driving patterns in both rainy and clear weather conditions. • This study showed how the trajectory analysis helped in better discriminating driving patterns during a specific event. • The usage of an advanced machine learning classification method helped in detecting risky driving patterns more efficiently. • It was found that using a cluster analysis helped in detecting the risky driving pattern over the whole events. • The cluster analysis succeeded to provide results in the same context as the trajectory analysis.

An LPV Approach to Autonomous Vehicle Path Tracking in the Presence of Steering Actuation Nonlinearities

This article deals with trajectory tracking for autonomous cars during evasive maneuvers and in the presence of steering actuator nonlinearities. This article develops an LPV MISO H-infinity controller based on the feedback of the lateral error at the center of gravity and the look-ahead distance. The controller architecture offers a way to cope with the effect of the steering nonlinearities, by scheduling one of the control weighting functions. A detailed experimental validation on three different maneuvers (straight driving, wide bend, and a double-lane change) shows the effectiveness of the proposed LPV solution.

Cooperative Path Following Control of Fixed-wing Unmanned Aerial Vehicles with Collision Avoidance

In this paper, we propose a novel curved path following scheme for multiple fixed-wing unmanned aerial vehicles (UAVs) that can achieve coordinated curved path following and handle collision avoidance simultaneously. The proposed solution is a hybrid system that combines a path following strategy and a collision avoidance method to ensure collision-free maneuvering of a group of UAVs cooperative flight. A strategy based on a virtual structure and a kinematic model is proposed to derive the cooperative curved path following. Meanwhile, a possible collision will occur when multi-UAVs flight in the form of dense formations, or there are path intersections when the formation is changed. However, all fixed-wing vehicles need to maintain a minimum airspeed and cannot stop before the collision. Therefore, there is a need for a fast and efficient method that can be implemented online, taking into account the physical limitations of the vehicle (minimum speed, turning rate, etc.) to avoid possible collision. We employ a modified vector field histogram (VFH) method to provide real-time collision avoidance, and evasive maneuvers work only if the distance is within the conflict zone. In order to verify the proposed control scheme, dense formation flight tests of small fixed-wing UAVs were implemented.

A new tool for conjunction analysis of ISRO's operational satellites, Close Approach Prediction Software: CLAPS

During the lifetime of an operational spacecraft, a situation may occur when it faces a close approach with other orbiting space objects. The mitigation strategy for minimizing threat from orbiting space objects is to first carry out proximity analysis for operational spacecraft's (primary) with all other catalogued orbiting space objects (secondary). In case of a possible close approach, to plan an evasive collision avoidance maneuver. The ever-increasing number of space objects around the Earth demands this kind of analysis on daily basis by satellite operators. Presently, ISRO is operating close to 50 satellites in LEO and GEO/GSO orbits and this number is increasing each year at a rapid rate. At present the NORAD TLE catalogue consists of more than 18000 unclassified space objects. The large number of object pairs require enormous amount of computational load to do such kind of analysis on daily basis. Therefore, an efficient and user-friendly tool to predict the future close approaches is necessary for satellite operators.In this paper, methodology is designed and developed for carrying out conjunction analysis for all operational satellites with catalogued space objects. In the design, efficient approach is adopted to reduce the computation time. Each object pair goes through screening process using pre-filters like perigee-apogee test and smart sieves. These filters are based on basic flight dynamics rules. Only those pairs which are passed by all filters are subjected to relative distance function method for finer assessment. For the candidate pairs which violate the specified minimum Inter-Satellite-Distance (ISD) limit, collision probability is computed. For operational satellites, latest available orbit determination results at control centre are used and orbit propagation is done with high fidelity ephemeris model. Orbit propagation of catalogued objects with TLE is done using SGP4 model. Using this design methodology, Close Approach Prediction Software (CLAPS) is developed in C++ language, to predict the future close approaches for multiple operational satellites with complete TLE catalogue in single run.CLAPS s/w is tested for various close encounter cases and results are validated with STK's AdvCAT tool. The close approach time and minimum distance are found to match up to millisecond and millimetre level respectively. The results of few actual close approach scenarios are presented and discussed. CLAPS is being used regularly at ISRO's Master Control Facility, Hassan for routine monitoring of close approaches.

Determining the steering direction in critical situations: A decision tree–based method

Objective: Factors related to the driver–vehicle–environment system have a significant influence on a driver’s decision to perform evasive maneuvers, especially the decision of steering direction (DSD) in critical situations. However, few studies have systematically investigated the relationships between these factors and DSD. The objective of this study is to analyze and model drivers’ DSD in critical situations.Methods: Data from the NASS-CDS from 1995 to 2015 were utilized in this study. The decision tree (DT) classifier was utilized to model a driver’s DSD for both intersection-related and non-intersection-related subsets, combined with a 10-fold cross-validation technique and grid search approach to evaluate and optimize the model. An analysis of variable importance was also conducted.Results: Two separate DT models of drivers’ DSD were obtained based on the optimized hyperparameters, with test accuracies of 84.6% (intersection-related) and 79.2% (non-intersection-related). The variable DIFFANGLE (angle difference between 2 vehicles) ranked as the most important factor influencing drivers’ DSD in both models. The variables, in order of importance, were SPEED (travel speed of the subject vehicle) and AGE (driver’s age) for the intersection-related model and SPEED, PREMOVE (pre-event movement), TRAFFLOW (trafficway flow), and AGE for the non-intersection-related model. Moreover, an interesting same direction pattern was observed in both DT models.Conclusions: This study employed NASS-CDS data and DT classifiers to analyze and model drivers’ DSD behavior. The test accuracies for both classifiers were acceptable. Potential variables influencing drivers’ DSD were explored, which improves the research on evasive behavior in lateral movement and promotes further applications for intelligent vehicles using the constructed models.

The Power of Second-Order Legal Consciousness: Authorities’ Perceptions of “Street Policy” and Welfare Fraud Enforcement

Legal authorities’ second-order legal consciousness—their perceptions of others’ understandings of law—shapes the social realization of legal power. Analysis of interviews with welfare fraud enforcement workers from five US states reveals their perceptions of how clients view law, policy, and enforcement practices, and shows these perceptions’ consequences. Enforcement workers’ perceptions influence the discretionary work of policy implementation, as fraud workers attempt to circumvent what they see as clients’ evasive maneuvers and act in ways they believe will influence clients’ thinking and behavior. Fraud workers’ second-order legal consciousness can also influence welfare law, when their perceptions of clients’ understandings and behaviors drive changes in written rules. Together, these effects demonstrate the power of authorities’ second-order legal consciousness to affect both law in action and law in books. Through documenting the impact of authorities’ second-order legal consciousness, this study fills an important gap in social scientific knowledge of how ongoing, dynamic processes of assessing others’ thinking and responding accordingly shape law-infused environments.

Tactical preference based objectives for solving an evasion problem of fighter in air combat

Evasive maneuvers of fighter only consider the objective of miss distance in traditional air combat decision problems. The amalgamative tactical demands of achieving self-conflicting evasive objectives in actual air combat are taken into account in this paper. A method to generate a strategy of evasive maneuvers based on tactical preference is proposed. Tactical preference is given to higher miss distance, less energy consumption, and higher terminal superiority. The evasion problem in air combat is defined and reformulated into a multi-objective optimization problem, which is solved by a multi-objective evolutionary algorithm. Simulation results show the feasibility and effectiveness of the proposed method, and a set of approximate Pareto-optimal solutions are obtained to satisfy the tactical preference.

Satellite Relative Motion Modeling and Estimation via Nodal Elements

In this paper, a new parameterization of the relative motion between two satellites orbiting a central body is presented. The parameterization is based on the nodal elements: a set of angles describing the orbit geometry with respect to the relative line of nodes. These are combined with classical orbital elements to yield a nonsingular relative motion description. The exact nonlinear, perturbed dynamic model resulting from the new parameterization is established. The proposed parameter set captures the fundamental Keplerian invariants, while retaining a simple relationship with local orbital coordinates. An angles-only relative navigation filter and a collision avoidance scheme are devised by exploiting these features. The navigation solution is validated on a case study of an asteroid flyby mission. It is shown that a collision can be detected early on in the estimation process, which allows one to issue a timely evasive maneuver.

Mechanical work performed by distal foot-ankle and proximal knee-hip segments during anticipated and unanticipated cutting

Side-step cutting is a common evasive maneuver which is typically performed without prior anticipation. Studies quantifying joint work and its inter-joint proportions in cutting have not accounted for work done by the foot, even though this segment has been shown to be an important source of mechanical work in walking, running, and landing. The aims of this study were to: (1) quantify the magnitude of foot work performed and provide a more precise account of percentage joint work during cutting, and (2) examine the effect, a lack of anticipation had on these variables. Three-dimensional motion capture with forceplates were used to assess the cutting behaviour of 17 healthy participants. All participants performed a 45° cut with an approach speed of 4 m/s. Hip, knee, and ankle joint work were calculated using inverse dynamics; whilst foot work was quantified using the Unified-Deformable foot method. The foot contributed up to 12.45% and 3.09% of total limb negative and positive work, respectively. Unanticipated cutting significantly reduced ankle positive work (-0.09 J/kg [95% CI −0.13 to −0.06], P < 0.001) and significantly reduced percentage ankle positive work (-2.17% [95% CI −3.47 to −0.86], P = 0.001). The foot performs as much negative work as the hip but had only a minor contribution to positive work during cutting. Anticipation had a negligible influence on joint work and its inter-joint proportions. The foot should not be neglected in understanding whole-body dynamics during cutting, with greater understanding of its function potentially useful for informing athletic footwear design and cutting technique modification.