Evasive Maneuvers Research Articles

Lateral Evasive Maneuver with Shared Control Algorithm: A Simulator Study.

Shared control algorithms have emerged as a promising approach for enabling real-time driver automated system cooperation in automated vehicles. These algorithms allow human drivers to actively participate in the driving process while receiving continuous assistance from the automated system in specific scenarios. However, despite the theoretical benefits being analyzed in various works, further demonstrations of the effectiveness and user acceptance of these approaches in real-world scenarios are required due to the involvement of the human driver in the control loop. Given this perspective, this paper presents and analyzes the results of a simulator-based study conducted to evaluate a shared control algorithm for a critical lateral maneuver. The maneuver involves the automated system helping to avoid an oncoming motorcycle that enters the vehicle's lane. The study's goal is to assess the algorithm's performance, safety, and user acceptance within this specific scenario. For this purpose, objective measures, such as collision avoidance and lane departure prevention, as well as subjective measures related to the driver's sense of safety and comfort are studied. In addition, three levels of assistance (gentle, intermediate, and aggressive) are tested in two driver state conditions (focused and distracted). The findings have important implications for the development and execution of shared control algorithms, paving the way for their incorporation into actual vehicles.

Predicting occupant head displacements in evasive maneuvers; tuning and comparison of a rotational based and a translational based neck muscle controller.

Objective: Real-life car crashes are often preceded by an evasive maneuver, which can alter the occupant posture and muscle state. To simulate the occupant response in such maneuvers, human body models (HBMs) with active muscles have been developed. The aim of this study was to implement an omni-directional rotational head-neck muscle controller in the SAFER HBM and compare the bio-fidelity of the HBM with a rotational controller to the HBM with a translational controller, in simulations of evasive maneuvers. Methods: The rotational controller was developed using an axis-angle representation of head rotations, with x, y, and z components in the axis. Muscle load sharing was based on rotational direction in the simulation and muscle activity recorded in three volunteer experiments in these directions. The gains of the rotational and translational controller were tuned to minimize differences between translational and rotational head displacements of the HBM and volunteers in braking and lane change maneuvers using multi-objective optimizations. Bio-fidelity of the model with tuned controllers was evaluated objectively using CORrelation and Analysis (CORA). Results: The results indicated comparable performance for both controllers after tuning, with somewhat higher bio-fidelity for rotational kinematics with the translational controller. After tuning, good or excellent bio-fidelity was indicated for both controllers in the loading direction (forward in braking, and lateral in lane change), with CORA scores of 0.86-0.99 and 0.93-0.98 for the rotational and translational controllers, respectively. For rotational displacements, and translational displacements in the other directions, bio-fidelity ranged from poor to excellent, with slightly higher average CORA scores for the HBM with the translational controller in both braking and lane changing. Time-averaged muscle activity was within one standard deviation of time-averaged muscle activity from volunteers. Conclusion: Overall, the results show that when tuned, both the translational and rotational controllers can be used to predict the occupant response to an evasive maneuver, allowing for the inclusion of evasive maneuvers prior to a crash in evaluation of vehicle safety. The rotational controller shows potential in controlling omni-directional head displacements, but the translational controller outperformed the rotational controller. Thus, for now, the recommendation is to use the translational controller with tuned gains.

Passenger muscle responses in emergency braking events with reclined seating

Emergency braking can generate forward displacement that may influence the effectiveness of protection in collisions, especially for passengers. The development of automated vehicles has enabled the diversification and rationalization of sitting positions, including reclined seating. However, the passenger response in pre-crash scenarios in reclined seating differs from that in standard seating, which poses different requirements for biofidelic human body models (HBMs) to evaluate passenger injuries in collisions. This study conducted emergency braking trials in vehicles at an initial velocity of 80 km/h. Five volunteers were exposed to approximately 1 g manual emergency braking (MEB), and the muscle responses at the front passenger seat with backrest angles of 25°, 45°, and 65° were recorded. The electromyography obtained from 14 muscles of the neck, torso, and lower extremity were normalized using maximum voluntary contractions (MVCs). In the quiet sitting phase, the activity levels were low (< 5% MVC) in all muscles for the three sitting positions. During emergency braking, the muscles are activated to restrict the body motion. There were differences in muscle amplitude and onset time in different backrest angles, with higher muscle activity levels in most muscles in a reclined sitting position. In particular, the sternocleidomastoid, rectus abdominis, and vastus medialis showed different patterns in the peak and steady-state phases. We found that the tibialis anterior was consistently activated at a lower level in all sitting postures (< 8% MVC), which indicates limited support of the shank for the body. The data provided in the paper are presented in corridors and intended for use in the development and validation of HBMs with active muscle models to simulate evasive maneuvers that potentially occur before a crash in the reclined sitting position.

Технологія керування безпілотним літальним апаратом за умов конфліктних ситуацій

Introduction. The motion control of traditional manned aircraft and unmanned aerial vehicles (UAVs) has many distinctions that directly affect the process of conflict resolution. The problem of resolving conflict situations involving different types of aircraft is relevant due to the rapid development of unmanned aircraft and the expansion of its application areas. Purpose. The aim of the paper is to develop a technology for controlling an unmanned aerial vehicle in conflict situations, which will allow optimizing the maneuver trajectory based on a number of criteria and ensure safe separation of aircraft in airspace. Results. The paper proposes a technology for UAV control in the conditions of a conflict: a number of approaches to resolving conflict situations between different types of aircraft are identified; the concept of conflict situations for UAVs and traditional manned aircrafts is presented, on the basis of which a technology is developed that combines different approaches to detecting a conflict, classifying the occurrence of a conflict, classifying an evasive maneuver, and ensuring the safe separation of the controlled UAV from different types of aircraft in airspace. Conclusions. Based on the analysis results of the current state of UAV control in the conditions of a conflict problem, it is determined that such tasks are already being solved, but it is advisable to analyze the possibility of further providing additional protection for the controlled UAV and optimizing its trajectory to ensure the least spatial and time losses during the flight. Conflict situations are a violation of the norms of maintaining a safe distance in space and lead to a collision of aircraft in the absence of appropriate actions to resolve such violations. Therefore, for the effective resolution of the conflict situations, the classifications of the degree of threat, classification of evasive maneuvers and ensuring minimal spatial and time losses by using the criterion of minimum area of the required maneuver are proposed.

Hierarchical Reinforcement Learning for Submarine Torpedo Countermeasures and Evasive Manoeuvres

Hierarchical Reinforcement Learning for Submarine Torpedo Countermeasures and Evasive Manoeuvres

Decision‐Making and Path Planning for Head‐On Collision Avoidance on Curved Roads

Deviating to the left on two‐way roads can result in fatal head‐on collisions. This article presents an intelligent decision‐making and path‐planning algorithm aimed at avoiding collision with a vehicle that has deviated from the opposing lane. The path‐planning process utilizes the model predictive control (MPC) approach, employing a linear kinematic prediction model with a horizon of 2 seconds. Considering that the deviated vehicle may abruptly return to its original lane at any moment, its motion is associated with significant uncertainty. To address this challenge, the path‐planning algorithm directs the ego vehicle (EV) under specific constraints to ensure that both the left and right sides of the road are symmetrically reachable in future time steps. This enables the decision‐making algorithm to select the safer direction for evasive maneuver at the appropriate moment. The motion prediction of the threat vehicle (TV) is conducted until the potential collision time, taking into account its motion history, and is utilized in the decision‐making process. Once the maneuver direction is determined, the collision‐free path planning continues using the MPC method. To evaluate the algorithm, six simulations are conducted, modeling various distant and close encounter states of the vehicles on roads with left‐ and right‐hand curves. The simulation results indicate the flexibility and appropriate performance of the algorithm in planning safe and maneuverable paths.

Missile Threat Detection and Evasion Maneuvers With Countermeasures for a Low-Altitude Aircraft

This paper investigates Man-Portable Air Defense Systems (MANPAD) missile threat detection and an effective maneuvering strategy for aircraft (A/C) destruction avoidance. The 3D trajectory of a multistage missile (boost and sustain phases) under Pure Proportional Navigation is first estimated. The maximum likelihood estimator is used to estimate its motion parameters of the two-stage missile considered using only angle measurements from a low-altitude A/C. Missile threat detection is investigated based on two methods: estimation-based as a baseline method and threat plane-based. The former can provide a miss distance between the A/C and the missile, but more measurements are required. The latter is shown to give a faster threat alert, allowing the A/C to start the evasive maneuver earlier. An efficient evasion maneuver for avoiding destruction, is banking away from the missile by a turning maneuver in the horizontal plane, due to its low-altitude. The survivability probability of the A/C is evaluated with and w/o countermeasures (flares). There are two key factors that affect the evasion performance: maneuver start time and the maximum acceleration during the maneuver. Simulations show that the A/C can start maneuvering earlier using the threat plane-based method compared with the estimation-based method. It can successfully evade the missile with the help of maneuvering. It is also shown that using flares, the necessary maneuver is less drastic (lower <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$g$</tex-math></inline-formula> 's). Simulation results compare the necessary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$g$</tex-math></inline-formula> 's for obtaining a certain minimal distance from the missile.

Reactive and Predictive Control Scheme for Evasive Maneuvers in Aerial Robots

Reactive and Predictive Control Scheme for Evasive Maneuvers in Aerial Robots

Anisotropic contractile particle model with avoidance for simulating pedestrian navigation in dilute and dense systems

We introduce a novel rule-based pedestrian simulation model that extends an existing one by incorporating enhanced particle contact rules and evasive maneuver capabilities for collision avoidance. The model is calibrated using individual trajectory data from avoidance experiments. With the same set of parameters, the new model satisfactorily replicates experimental observations, including the avoidance behavior of individual agents, fundamental diagrams of unidirectional and bidirectional flows, as well as specific flow rates through bottlenecks.

Driver-initiated take-overs during critical evasion maneuvers in automated driving

The aim of the current study is to investigate predictors and consequences of driver-initiated take-overs during automated evasion maneuvers. Literature on control transitions in automated driving has mainly focused on system-initiated take-overs. However, drivers may also initiate take-overs without take-over requests. To date, such driver-initiated take-overs have rarely been investigated. Our study addresses this research gap.In a driving simulator study with 61 participants, we investigated whether the criticality of highly dynamic evasion maneuvers and trust in automation affect the probability of driver-initiated take-overs. Criticality was manipulated via time headway (THW) and traction usage (TU). Trust was varied by manipulating automation reliability before the experimental trials. Consequences of driver-initiated take-overs in terms of collisions and lane departures were assessed.The results indicate that THW, TU, and trust affect the probability of driver-initiated take-overs. Moreover, the time it takes the automation to respond to an obstacle ahead by starting an evasion maneuver may be another relevant factor in predicting take-overs. After a take-over, drivers produced a number of unnecessary lane departures and collisions. These were independent of THW and TU.The study demonstrates that drivers are more likely to take over vehicle control during automated evasion maneuvers without take-over requests when criticality increases and trust in automation decreases. Such take-overs may be hazardous for traffic safety. Our findings help to design automated vehicles that avoid unnecessary take-overs in critical driving situations or de-escalate their consequences effectively, thus increasing traffic safety.

Effectiveness of the Autonomous Braking and Evasive Steering System OPREVU-AES in Simulated Vehicle-to-Pedestrian Collisions

This paper proposes a combined system (OPREVU-AES) that integrates optimized AEB and Automatic Emergency Steering (AES) to generate evasive maneuvers, and it provides an assessment of its effectiveness when compared to a commercial AEB system. The optimized AEB system regulates the braking response through a collision prediction model. OPREVU is a research project in which INSIA-UPM and CEDINT-UPM cooperate to improve driving assistance systems and to characterize pedestrians’ behavior through virtual reality (VR) techniques. The kinematic and dynamic analysis of OPREVU-AES is conducted using CarSim© software v2020.1. The avoidance trajectories are predefined for speeds above 40 km/h, which controls the speed and lateral stability during the overtaking and lane re-entry process. In addition, the decision algorithm integrates information from the lane and the blind spot detectors. The effectiveness evaluation is based on the reconstruction of a sample of vehicle-to-pedestrian crashes (INSIA-UPM database), using PCCrash© software v. 2013, and it considers the probability of head injury severity (ISP) as an indicator. The incorporation of AEB can avoid 53.8% of accidents, with an additional 2.5–3.5% avoided by incorporating automatic steering. By increasing the lateral activation range, the total avoidance rate is increased to 61.8–69.8%. The average ISP reduction is 65%, with significant reductions achieved in most cases where avoidance is not possible.

Low-Cost Computer-Vision-Based Embedded Systems for UAVs

Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype’s efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement—cutting costs, expanding terrain coverage, and boosting surveillance capabilities.

Investigating sources for variability in volunteer kinematics in a braking maneuver, a sensitivity analysis with an active human body model.

Occupant kinematics during evasive maneuvers, such as crash avoidance braking or steering, varies within the population. Studies have tried to correlate the response to occupant characteristics such as sex, stature, age, and BMI, but these characteristics explain no or very little of the variation. Therefore, hypothesis have been made that the difference in occupant response stems from voluntary behavior. The aim of this study was to investigate the effect from other sources of variability: in neural delay, in passive stiffness of fat, muscle tissues and skin, in muscle size and in spinal alignment, as a first step towards explaining the variability seen among occupants in evasive maneuvers. A sensitivity analysis with simulations of the SAFER Human Body Model in braking was performed, and the displacements from the simulations were compared to those of volunteers. The results suggest that the head and torso kinematics were most sensitive to spinal alignment, followed by muscle size. For head and torso vertical displacements, the range in model kinematics was comparable to the range in volunteer kinematics. However, for forward displacements, the included parameters only explain some of the variability seen in the volunteer experiment. To conclude, the results indicate that the variation in volunteer vertical kinematics could be partly attributed to the variability in human characteristics analyzed in this study, while these cannot alone explain the variability in forward kinematics. The results can be used in future tuning of HBMs, and in future volunteer studies, when further investigating the potential causes of the large variability seen in occupant kinematics in evasive maneuvers.

Hierarchical Goal-Guided Learning for the Evasive Maneuver of Fixed-Wing UAVs based on Deep Reinforcement Learning

Hierarchical Goal-Guided Learning for the Evasive Maneuver of Fixed-Wing UAVs based on Deep Reinforcement Learning

A Decision Support System Using Fuzzy Logic for Collision Avoidance in Multi-Vessel Situations at Sea

The increasing traffic and complexity of navigation at sea require advanced decision support systems to ensure greater safety. In this study, we propose a novel decision support system that employs fuzzy logic to improve situational awareness and to assist navigators in collision avoidance during multi-vessel encounters. The system is based on the integration of the rules of the Convention on International Regulations for Preventing Collisions at Sea (COLREGs) and artificial intelligence techniques. The proposed decision model consists of two main modules to calculate the initial encounter conditions for the target vessels, evaluate the collision risk and navigation situation based on COLREG rules, sort the target vessels, and determine the most dangerous vessel. Fuzzy logic is used to calculate the collision avoidance maneuver for the selected ship, considering the closest point of approach, relative bearing, and the ship’s own speed. Simulation tests demonstrate the effectiveness of the fuzzy-based decision model in scenarios with two ships. However, in complex situations with multiple ships, the performance of the model is affected by possible conflicts between evasive maneuvers. This highlights the need for a cooperative collision avoidance algorithm for all vessels in high traffic areas.

Calibrated microphone array recordings reveal that a gleaning bat emits low-intensity echolocation calls even in open-space habitat.

Echolocating bats use ultrasound for orientation and prey capture in darkness. Ultrasound is strongly attenuated in air. Consequently, aerial-hawking bats generally emit very intense echolocation calls to maximize detection range. However, call levels vary more than tenfold (>20 dB) between species and are tightly linked to the foraging strategy. The brown long-eared bat (Plecotus auritus) is a primarily gleaning, low-amplitude species that may occasionally hawk airborne prey. We used state-of-the-art calibrated acoustic 3D-localization and automated call analysis to measure P. auritus' source levels. P. auritus emits echolocation calls of low amplitude (92 dB rmsSPL re 20µPa @ 10cm) even while flying in open-space. While P. auritus thus likely benefits from delayed evasive manoeuvres of eared insects, we propose that low-amplitude echolocation did not evolve as an adaptive countermeasure, but is limited by morphological constraints.

Effectiveness analysis of a hard-kill underwater defense system for surface warships against wake-homing torpedo attack

We conducted simulations to analyze the effects of a hard-kill-type underwater defense system that defends friendly warships against an enemy wake-homing torpedo. Assuming that the enemy torpedo is a wake-homing torpedo, our surface warship detours to the prespecified evasive course by firing a hard-kill-type system, which is modeled as a passive acoustic homing-torpedo, to attack the enemy torpedo. We analyzed the effectiveness of a warship’s survival probability via Monte Carlo simulation, given the probabilistic angles of the launched torpedoes, to compare two cases where one used only evasive maneuvering and the other used the hard-kill-type underwater defense system with evasion at the same time. By changing the maximum torpedo detection range of a warship and the torpedo’s initial location, we observed that the resulting survival probability of a warship was above 61% with a hard-kill-type defense system, whereas it remained at 34% without a hard-kill defense system, the necessity of a hard-kill underwater defense system, especially against wake-homing torpedoes.

Evaluation of intersection crashes using naturalistic driving data through the lens of future I-ADAS

Objective Intersection advanced driver assistance systems (I-ADAS) with the capability to detect possible collisions and perform evasive braking have the potential to reduce the number of intersection crashes. However, these systems will encounter many challenges caused by the complexity of real-world driving conditions. The purpose of this study is to use real-world naturalistic driving data to conduct an initial exploration of the potential challenges for future I-ADAS in straight crossing path (SCP), left turn across path/lateral direction (LTAP/LD), and left turn across path/opposite direction (LTAP/OD) crash configurations. Methods Intersection crashes were selected from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. The SHRP 2 dataset includes front-facing, driver-facing, rear-facing, and a hands/feet-facing video and vehicle speed, steering, accelerator, and brake time-series data. This data was reviewed to understand driver sightline obstructions, driver distractions, and initiation of driver responses. The estimated time to collision (TTC) from the precipitating event, defined as when either vehicle entered the intersection without the right-of-way, was computed based on the distance to the impact point divided by the current velocity of the subject vehicle. Results The median impact speed was 18.0 km/h for SCP and LTAP/LD crashes and 16.1 km/h for LTAP/OD crashes. The median TTC from the precipitating event was 1.35 s for SCP and LTAP/LD crashes and 1.44 s for LTAP/OD crashes. For SCP crashes, the three main sightline obstruction scenarios were slower vehicles traveling in the same direction waiting to turn right, vehicles in the closer crossing lane, and a parked truck. For LTAP/OD crashes, the sightline obstruction was often oncoming vehicles in a closer lane blocking the view of another vehicle. Conclusion Sightline obstructions could present a challenge for future I-ADAS to activate in SCP, LTAP/LD, and LTAP/OD crashes. This study utilized naturalistic driving data to complete a comprehensive analysis of intersection crashes, including driver distractions, evasive maneuvers, and sightline obstructions that can assist in the development of I-ADAS. This analysis is not possible with police-reported crash data only, which does not contain necessary details on the driver and surrounding environment.

Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System.

The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. This paper proposes a vision-based in-flight collision avoidance system based on background subtraction using an embedded computing system for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) subtract dynamic background subtraction to remove it and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) cluster the moving objects and remove noise blobs, using Euclidean clustering, (iv) distinguish independent objects and track the movement using the Kalman filter, and (v) avoid collision, using the proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast-moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace a high-vision system such as event camera. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via a two-dimensional transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the three-dimensional trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a test quadrotor UAV, and appropriate algorithm parameters for various requirements are deduced.

Do nocturnal birds use acoustic and visual cues to avoid predation by bats?

Anti-predation strategies are critical to animal survival and are fundamental to deciphering predator-prey interactions. As an important defense strategy, sensory predator detection (such as through acoustic and visual cues) enables animals to assess predation risk and execute predator-avoidance behavior; however, there are limited studies on the anti-predation behavior of nocturnal animals. The prey of bats provides an excellent representative system for examining the anti-predation behavior of nocturnal animals. Here, we broadcasted different types of echolocation calls of the bird-eating bat Ia io to two wild passerine birds, namely, Zosterops japonicus and Sinosuthora webbiana, that are preyed upon by I. io, and presented the birds with individual bats under different light intensities. The results showed that both bird species were able to perceive the low-frequency audible portion of the bats' echolocation calls; however, they did not exhibit escape responses to the acoustic stimuli. In the dark and under moonlit conditions, both bird species were unable to respond to active bats at close range and the birds only exhibited evasive flight behavior when bats approached or touched them. These results suggest that nocturnal passerine birds may not be able to use acoustic or visual cues to detect bats and adopt evasive maneuvers to avoid predation. This work suggests that bat predation pressure may not elicit primary predator-avoidance responses in nocturnal passerine birds. The results provide new insights into the anti-predation behavior of nocturnal animals.