Collision Scenarios Research Articles

Bearing the weight: A qualitative study on expert views on integrating ethics in autonomous vehicles

AVs are increasingly becoming present on public roads, the ethics of which have captured the publics attention. This study represents the first ever investigation into the opinion of AV development experts on the ethics of programming AVs in collision scenarios. The study makes use of a qualitative research model. A sample of 24 Swiss experts were interviewed using semi-structured interviews. The majority of experts were directly involved in the development of AVs and represented both research, private, and public sectors as well as national regulatory bodies. Three core themes emerged from the data. 1) Experts were keenly aware of the ethical challenges involved in AVs on public roads, including in collision situations. 2) Experts felt a moral responsibility for how AVs are programmed and developed. However, experts’ moral framework was not well developed with many experts stating that their own personal moral framework should not be used to program motion planning. 3) Experts saw a need for an ethical framework to guide the development of AVs. The paper concludes that it is clear experts are keenly aware of the ethics involved in AVs on public roads, and in particular collision scenarios. They feel a sense of responsibility for how AVs are programmed but do not feel ethically well-equipped themselves. Experts would, therefore, appreciate an ethical framework that would assist the development of these algorithms. This framework may be driven by either industry or government but should be clear and simple. We recommend countries like Switzerland develop such a framework.

Vehicle-to-Cyclist Collision Prediction Models by Applying Machine Learning Techniques to Virtual Reality Bicycle Simulator Data

The study of vulnerable road users (VRUs) behavior is key to designing and optimizing driving assistance systems, such as the autonomous emergency braking (AEB) system. These kinds of devices could help lower the VRU accident rate, which is of particular interest to cyclists, who are the subject of this research. To better understand cyclists’ reaction patterns in frequently occurring collision scenarios in urban environments, this paper focuses on developing a virtual reality (VR) simulator for cyclists (VRBikeSim) that incorporates eye-tracking functionality. The braking and steering systems were calibrated by means of on-track tests with a sensorized bicycle in order to improve the accuracy of the bicycle virtual model. From the data obtained in the virtual tests, a battery of predictive models was built using supervised machine learning classifiers. All of them exhibited an accuracy higher than 85%, especially the K-Nearest Neighbors model. This model allowed us to obtain the best balance between the prediction of avoidance and collision cases, as well as enabling computationally lower times to be incorporated into the decision-making algorithm of an AEB system.

Risk Analysis and Visualization of Merchant and Fishing Vessel Collisions in Coastal Waters: A Case Study of Fujian Coastal Area

The invasion of ship domains stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying the collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validates it through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of the collision risk. Finally, taking the high-risk area for merchant and fishing vessel collisions in the Minjiang River Estuary as an example, this paper conducts an analysis that involves classifying ship collision scenarios, extracts risk data under different collision scenarios, and visually analyzes areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis are verified by the number of accidents that occurred in the most recent three years.

Experiments and design of a kirigami-based multi-stage energy absorption structure subjected to axial impact

As a typical energy-absorbing structure, the thin-walled structure dissipates impact energy through the formation of plastic hinges and material failure. The geometric configuration of the energy-absorbing structure deeply influences its response to impact loads. For a certain load curve required by collision scenarios, this study proposes a novel design method for approximating a continuous impact load curve with discretized ones. Based on kirigami structures, the conceptual framework integrates multi-module and multi-stage designs to regulate segmental stiffness through theoretical analysis, thereby achieving the desired curve approximation. The effectiveness of the proposed kirigami-based multi-stage energy absorption structure (KMS) is validated by drop-weight impact tests and numerical simulations. Findings demonstrate that the KMS effectively dissipates impact energy in a progressive manner, aligning with the desired load curve and inducing stable and orderly structural deformation under axial impact. Through appropriate parameter design, the deformation mode and the inducing effect of the kirigami structure can be managed, enabling the structure's adaptability to diverse collision scenarios.

Unravelling the collision scenario of the dissociative galaxy cluster Abell 56 through hydrodynamic simulations

ABSTRACT In galaxy cluster collisions, the gas can be separated from dark matter haloes. Abell 56 displays signatures of a dissociative bullet-like merger with a possible high-inclination angle between the plane of orbit and the sky. Our objective is to provide a comprehensive description of the features observed in the collision scenario of Abell 56. Additionally, we aim to apply a potential weak lensing mass bias correction attributed to the merger to evaluate its impact on our findings. To investigate this, we perform tailored hydrodynamical N-body simulations, varying the impact parameter. We initially identified an early scenario at 0.12 Gyr after the central passage that reproduces some observational features. However, the mean temperature of 9.7 keV exceeded the observed value. Our best model corresponds to the late scenario at 0.52 Gyr after the pericentre, reproducing observed features of Abell 56, with an inclination of 58°. These features include the offset of 103 kpc between the main gas density peak and the south dark matter density peak, gas morphology, a line-of-sight relative velocity of 184 km s−1, and a mean temperature of 6.7 keV. This late model provides a plausible scenario to describe the dynamics of Abell 56. The weak lensing mass bias did not significantly impact the overall dynamics of this cluster merger.

Evaluation of force pain thresholds to ensure collision safety in worker-robot collaborative operations.

With the growing demand for robots in the industrial field, robot-related technologies with various functions have been introduced. One notable development is the implementation of robots that operate in collaboration with human workers to share tasks, without the need of any physical barriers such as safety fences. The realization of such collaborative operations in practice necessitates the assurance of safety if humans and robots collide. Thus, it is important to establish criteria for such collision scenarios to ensure robot safety and prevent injuries. Collision safety must be ensured in both pinching (quasi-static contact) and impact (transient contact) situations. To this end, we measured the force pain thresholds associated with impacts and evaluated the biomechanical limitations. This measurements were obtained through clinical trials involving physical collisions between human subjects and a device designed for generating impacts, and the force pain thresholds associated with transient collisions between humans and robots were analyzed. Specifically, the force pain threshold was measured at two different locations on the bodies of 37 adults aged 19-32 years, using two impactors with different shapes. The force pain threshold was compared with the results of other relevant studies. The results can help identify biomechanical limitations in a precise and reliable manner to ensure the safety of robots in collaborative applications.

Minimum decision of collision injury based on drivable area in unavoidable scene

The existing technology for assisted driving collision avoidance has improved the active safety of vehicles, but it is primarily designed for scenarios where collisions can be avoided. It is not suitable for situations where pedestrians suddenly intrude. Therefore, there is a need to develop a control strategy that can minimize collision injuries in unavoidable situations. This study focuses on typical collision scenarios where pedestrians suddenly intrude. It proposes a real-time online double-layer optimal pre-collision control strategy based on drivable area, using the Head Injury Criteria ( HIC) as a quantifiable measure of collision injury. The upper-layer controller designs a cost function based on a three Degrees of Freedom (DOF) collision solving model to minimize collision injuries. It takes the constraints of vehicle dynamics, maximum front wheel steering angle, pedestrian avoidance into account, and the optimal pre-impact state point with the minimum HIC value is calculated in real-time. Meanwhile, the lower-layer controller is designed to track the optimal pre-impact state point using Model Predict Control (MPC) algorithm. The effectiveness of the proposed control strategy is verified through Simulink simulation, achieving the dual objectives of avoiding pedestrians suddenly intruding and minimizing passenger collision injuries.

Research on the centre-of-mass adjustment method for the head of an anthropometric test device based on a head impact biomechanics model

Head injury is the primary cause of serious lesions or death in automobile accidents. During crash tests, the mass and centre-of-mass position of the head of anthropometric test devices are crucial factors determining the accuracy of head injury test data. However, owing to the irregular shape as well as complex structure and material composition of the head of dummies, problems such as uneven mass distribution easily occur, resulting in deviations between the actual and designed positions of the centre-of-mass of the head. This study conducted research on head impact biomechanics and the centre-of-mass adjustment method for the head of dummies. Specifically, the biomimetic mechanical equivalent of the head was investigated, and head impact mechanics were analysed. Then, a predictive model was established for head drop mechanical response; the accuracy of this model was verified through head drop experiments. Based on the model, the law of centre-of-mass influence on the mechanical response of the dummy head was investigated, yielding an evaluation method for the different mechanical responses of the head. Using the rotational centre-of-mass measurement method, the mass and centre-of-mass position of the head were obtained. The distributed centre-of-mass adjustment method based on the principle of torque equilibrium was utilized to solve the deviations in the centre-of-mass position, the geometric dimensions of the counterweights and the centre-of-mass itself. Two counterweights were distributed on both sides of the internal cavity wall of the dummy head, resolving the positioning issue of the counterweights in the head cavity. The rationality of the centre-of-mass adjustment results was analysed using the predictive model for head impact mechanical response. The analysis results indicate that the centre-of-mass-adjusted dummy head better aligned with the mechanical response of the head in real collision scenarios, providing a new approach for the adjustment of the centre-of-mass of the head of dummies.

Cloud–Cloud Collision and Cluster Formation in the W5-NW Complex

We present a detailed structural and gas kinematic study of the star-forming complex W5-NW. A cloud–cloud collision scenario unravels with evidence of collision-induced star and cluster formation. Various signatures of cloud–cloud collision such as “complementary distribution” and “bridging features” are explored. At the colliding region, the two clouds have complementary morphologies, where W5-NWb has a filamentary key-like shape that fits into the U-shaped cavity in W5-NWa that behaves like a keyhole. The interaction region between the two clouds is characterized by bridging features with intermediate velocities connecting the two clouds. A skewed V-shaped bridging feature is also detected at the site of the collision. A robust picture of the molecular gas distribution highlighting the bridges is seen in the position–position–velocity diagram obtained using the SCOUSEPY algorithm. Star cluster formation with an overdensity of Class I and Class II young stellar objects is also seen towards this cloud complex, likely triggered by the cloud collision event.

SLoN: a spiking looming perception network exploiting neural encoding and processing in ON/OFF channels.

Looming perception, the ability to sense approaching objects, is crucial for the survival of humans and animals. After hundreds of millions of years of evolutionary development, biological entities have evolved efficient and robust looming perception visual systems. However, current artificial vision systems fall short of such capabilities. In this study, we propose a novel spiking neural network for looming perception that mimics biological vision to communicate motion information through action potentials or spikes, providing a more realistic approach than previous artificial neural networks based on sum-then-activate operations. The proposed spiking looming perception network (SLoN) comprises three core components. Neural encoding, known as phase coding, transforms video signals into spike trains, introducing the concept of phase delay to depict the spatial-temporal competition between phasic excitatory and inhibitory signals shaping looming selectivity. To align with biological substrates where visual signals are bifurcated into parallel ON/OFF channels encoding brightness increments and decrements separately to achieve specific selectivity to ON/OFF-contrast stimuli, we implement eccentric down-sampling at the entrance of ON/OFF channels, mimicking the foveal region of the mammalian receptive field with higher acuity to motion, computationally modeled with a leaky integrate-and-fire (LIF) neuronal network. The SLoN model is deliberately tested under various visual collision scenarios, ranging from synthetic to real-world stimuli. A notable achievement is that the SLoN selectively spikes for looming features concealed in visual streams against other categories of movements, including translating, receding, grating, and near misses, demonstrating robust selectivity in line with biological principles. Additionally, the efficacy of the ON/OFF channels, the phase coding with delay, and the eccentric visual processing are further investigated to demonstrate their effectiveness in looming perception. The cornerstone of this study rests upon showcasing a new paradigm for looming perception that is more biologically plausible in light of biological motion perception.

Risk analysis of rear-end collision scenario

Risk analysis of rear-end collision scenario

A viscous investigation on the hydrodynamic coefficients and wave loads under the interaction of side-by-side cylinders in regular waves

The paper introduces a two-dimensional Reynolds-Averaged Navier-Stokes Computational Fluid Dynamics (RANS CFD) model to investigate the effects of adjacently floating bodies on the hydrodynamic coefficients and wave loads acting on these bodies. The analysis considers two square cylinders with a narrow gap in way of the free water surface. The physical significance of subsections of the hydrodynamic coefficient matrix becomes evident when comparing coefficients obtained from oscillating-oscillating versus oscillating-fixed models. This understanding is significant, particularly in applications where only specific portions of the hydrodynamic coefficient matrix are utilized, such as in ship-ice collision scenarios (Jiang et al., 2023a). The correlation between wave loads and wave elevations reveals that the sway force and roll moment are associated with the disparity in wave elevation at both sides of each cylinder, while heave forces correlate with the averaged wave elevation on both sides of each cylinder. It is concluded that hydrodynamic interactions are sensitive to the displacement of a floater and accordingly can dominate added mass and damping effects.

ARGo: augmented reality-based mobile Go stone collision game

In this study, we present a mobile Go stone collision game based on augmented reality, which we call ARGo, inspired by the traditional Korean board game, Alkkagi. ARGo aims to resolve two main issues: (1) the portability and space constraints of the original Alkkagi and (2) the limited sense of reality due to the touchscreen-based interface of the existing mobile Alkkagi games. To improve a sense of the reality of the game, ARGo provides a gameplay interface similar to the original Alkkagi by recognizing the user‘s hand motion based on AR. Additionally, it provides a customization mechanism for each user to improve the recognition of the hand motion and the strength of the attack considering each user‘s characteristics. Finally, we make the following three main contributions. First, we employ the automata theory to design the game and collision scenarios between stones. Consequently, we can clearly define the complicated states incurred by AR-based motion recognition and collisions between virtual objects. Second, we propose a collision equation based on Continuous Collision Detection tailored to ARGo, i.e., Go stones and their collisions. Through experimental studies, we demonstrate that the collision equation enables the simulation of the exact collision effects. Third, through user experience studies, we verify the effectiveness of ARGo by showing the effects of the functions implemented in ARGo and its superiority over the existing mobile game Alkkagi Mania.

Passive Safety Assessment of Railroad Trains in Moose Herd Collision Scenarios

Moose herd–train collisions represent one of the potential hazards that railway operations must contend with, making the assessment of passive train safety in such scenarios a crucial concern. This study analyzes the responses of bullet trains colliding with moose herds and investigates the influence of various factors under these conditions. To achieve this goal, a multibody (MB) model was developed using the MADYMO platform. The displacement of the moose’s center of gravity (CG) was employed to assess the safety boundaries, while the relative positions between the wheels and rails were used to evaluate the risk of derailment. The findings revealed that the collision forces exhibited multi-peak characteristics that were subsequently transmitted to the wheel–rail contact system, resulting in disturbances in the relative positions of the wheels and rails. However, these disturbances did not reach a level that would induce train derailment. Furthermore, larger moose herds exhibited higher throw heights, although these heights remained within safe limits and did not pose a threat to overhead lines. The primary safety risk in moose–train collisions stemmed from secondary collisions involving moose that had fallen onto the tracks and oncoming trains. This study offers valuable insights for enhancing the operational safety of high-speed trains and safeguarding wildlife along railway corridors.

The risk ethics of autonomous vehicles: an empirical approach

How would people distribute risks of autonomous vehicles (AVs) in everyday road traffic? The rich literature on the ethics of autonomous vehicles (AVs) revolves around moral judgments in unavoidable collision scenarios. We argue for extending the debate to driving behaviors in everyday road traffic where ubiquitous ethical questions arise due to the permanent redistribution of risk among road users. This distribution of risks raises ethically relevant questions that cannot be evaded by simple heuristics such as “hitting the brakes.” Using an interactive, graphical representation of different traffic situations, we measured participants’ preferences on driving maneuvers of AVs in a representative survey in Germany. Our participants’ preferences deviated significantly from mere collision avoidance. Interestingly, our participants were willing to take risks themselves for the benefit of other road users, suggesting that the social dilemma of AVs may be mitigated in risky environments. Our research might build a bridge between engineers and philosophers to discuss the ethics of AVs more constructively.

Research on the Testing and Evaluation of Active Safety System for Turning Cars in Two wheeler Traffic Scenarios

China In-depth Traffic Accident Survey (CIDAS) shows that two wheeler has the highest accident fatality rate, more than 38%, which is far more than cars and pedestrians. In this paper, the characteristics of traffic accidents in China, the types of two wheeler, and traffic characteristics of two wheeler are taken into account. There are typical hazardous scenarios in the case of crossing, longitudinal, turning, mixed, etc. In the existing collision scenarios design of cars and two wheeler, the testing methods of crossing and longitudinal have been basically mature. Therefore, combining with the functional testing of the current automobile active safety system, to recurrent the traffic scenarios of two wheeler objectively, the active safety test and evaluation scenarios of turning cars under the two wheeler traffic scenarios are designed. Test paths, test speeds, and impact position are proposed. it complements the test scenarios for active safety function.

Collisions between Drones and Rotorcraft: Modeling of the Crash Response of Battery Packs

Drones operating in an urban environment pose a potential collision threat to rotorcraft. In this paper, the battery pack of the DJI MAVIC 2 ZOOM is analyzed since the battery is considered to be the greatest threat due to its high weight and stiffness. Following a pyramid-type building block approach, a high-fidelity model simulation was developed for LSDYNA based on a wide range of experiments, ranging from quasi-static material tests to quasi-static component tests up to high-velocity impact experiments. The high-fidelity model allows the prediction of damage in potential collision scenarios between a high-speed rotorcraft and the battery pack of the drone. For the particular impact configuration analyzed within this paper, the drone battery does not cause catastrophic failure of the windshield of the rotorcraft.

Dynamic Scheduling Optimization Method for Multi-AGV-Based Intelligent Warehouse Considering Bidirectional Channel

With the implementation of AGV technology and automated scheduling, storage and retrieval systems have become widely utilized in warehouse management. However, due to the use of unidirectional channels, AGV movement is restricted, and detours may occur frequently. Additionally, as the number of AGVs increases, deadlocks can arise, which lead to delays in order packaging and a decrease in overall warehouse performance. Hence, this paper proposes a dynamic scheduling method for task assignment and route optimization of AGVs to prevent collisions. The routing optimization method is based on an improved A* algorithm, which takes into account the dynamic map as input. Moreover, this paper investigates highly complex collision scenarios in bidirectional channels. Through simulation experiments, it is evident that scheduling methods based on bidirectional channels offer a clear advantage in terms of efficiency compared to those based on unidirectional channels.

Optimal Multi-Sensor Obstacle Detection System for Small Fixed-Wing UAVs

The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°.

Study of AEB and active seat belt on driver injury in vehicle–vehicle frontal oblique crash

The safety of vehicle occupants in oblique collision scenarios continues to pose challenges, even with the implementation of Automatic Emergency Braking (AEB) systems. While AEB reduces collision risks, studies indicate it may heighten injury risks for out-of-position (OOP) occupants. To counteract this issue, the integration of active seat belts in vehicles equipped with AEB systems is recommended. Firstly, this study established an oblique angle collision scenario post-AEB activation using data from the Chinese National Automobile Accident In-depth Investigation System (NAIS) database, analyzed through Prescan software. The dynamic response of the vehicle was examined. Following this, finite element (FE) models were validated to assess the effects of collision overlap rate, AEB braking strategy, and active seat belt pre-tensioning on occupant injuries and kinematics. Under specific collision conditions, the impact of the timing and amount of seat belt pre-tensioning, as well as airbag deployment timing on occupant injuries, was also explored. Findings revealed that a 75% collision overlap rate significantly increases driver injury risk. Active seat belts effectively mitigate injuries caused by OOP statuses during AEB interventions, with the lowest Weighted Injury Criterion (WIC) observed at a pre-tensioning time of 200 ms for active seat belts. The study further suggests that optimal results in reducing occupant injuries are achieved when active pre-tensioning seat belts are complemented by appropriately timed airbag deployment.