Vehicle Safety Research Articles

Frontal Impact Energy Absorbers for Passenger Cars.

Road accidents cause considerable losses to road users and to society. The steady increase in the number of vehicles leads to increased traffic volume. Therefore, there is a real need to improve passenger safety by developing passive safety systems. This article presents the results of experimental tests of structures absorbing kinetic energy, which could be used in the front section of a vehicle in order to reduce the consequences of passenger car head-on collisions. A number of crash tests of selected structures were conducted under various load conditions. An analysis was carried out of parameters enabling the authors to assess the level of energy absorption by the absorbers made, and compare these to absorbers available on the market. The tests carried out made it possible to determine energy absorption capability of the crash boxes prepared and to identify a structure exhibiting the most advantageous properties from the point of view of its prospective use. Of all of the absorbers analysed, in the context of energy absorption, it was the absorber made of glass-fibre-reinforced polyphenylene sulphide that produced the most advantageous results. Nonetheless, favourable results were obtained for all of the structures tested.

An Experimental Study of an Autonomous Heat Removal System Based on an Organic Rankine Cycle for an Advanced Nuclear Power Plant

The present study focuses on the recovery of waste heat in an autonomous safety system designed for advanced nuclear reactors. The system primarily relies on passive safety condensers, which are increasingly integrated into the design of advanced Pressurized Water Reactors (PWRs). These condensers are typically immersed in large water tanks that serve as heat sinks and are placed at sufficient heights to ensure natural circulation. Such a heat removal system can operate for an extended period, depending on the size of the tank. This research is driven by the potential to recover part of the energy stored in the boiling water volume, using it as a heat source for an Organic Rankine Cycle (ORC) system via an immersed heat exchanger. The electricity generated by the ORC engine can be used to power the system components, thereby making it self-sufficient. In particular, a pump replenishes the water tank, ensuring core cooling for a duration no longer limited by the water volume in the tank. An experimental test setup, including a boiling water pool and an ORC engine with an electrical output of approximately several hundred watts, along with an immersed evaporator, was constructed at CEA (Grenoble, France). Several test campaigns were conducted on the experimental test bench, exploring different configurations: two distinct ORC working fluids, cold source temperature variation effects, and relative positioning of the submerged evaporator and heat source within the water tank impact. These tests demonstrated the reliability of the system. The results were also used to validate both the ORC condenser and evaporator models. This article presents this innovative system, which has recently been patented. Moreover, to the best of our knowledge, the investigated configuration of an ORC that includes an immersed evaporator is original.

Axial Impact Response of Carbon Fiber-Reinforced Polymer Structures in High-Speed Trains Based on Filament Winding Process.

The continuous increase in the operating speed of rail vehicles demands higher requirements for passive safety protection and lightweight design. This paper focuses on an energy-absorbing component (circular tubes) at the end of a train. Thin-walled carbon fiber-reinforced polymer (CFRP) tubes were prepared using the filament winding process. Through a combination of sled impact tests and finite element simulations, the effects of a chamfered trigger (Tube I) and embedded trigger (Tube II) on the impact response and crashworthiness of the structure were investigated. The results showed that both triggering methods led to the progressive end failure of the tubes. Tube I exhibited a mean crush force (MCF) of 891.89 kN and specific energy absorption (SEA) of 38.69 kJ/kg. In comparison, the MCF and SEA of Tube II decreased by 21.2% and 21.9%, respectively. The reason for this reduction is that the presence of the embedded trigger in Tube II restricts the expansion of the inner plies (plies 4 to 6), thereby affecting the overall energy absorption mechanism. Based on the validated finite element model, a modeling strategy study was conducted, including the failure parameters (DFAILT/DFAILC), the friction coefficient, and the interfacial strength. It was found that the prediction results are significantly influenced by modeling methods. Specifically, as the interfacial strength decreases, the tube wall is more prone to circumferential cracking or overall buckling under axial impact.

Modelling of a Cylindrical Battery Mechanical Behavior under Compression Load

The extensive utilization of lithium-ion (Li-ion) batteries within the automotive industry necessitates rigorous measures to ensure their mechanical robustness, crucial for averting thermal runaway incidents and ensuring vehicle safety. This paper introduces an innovative methodology aimed at homogenizing the mechanical response of Li-ion batteries under compression load, using Finite Element Method (FEM) techniques to improve computational efficiency. A novel approach is proposed, involving the selective application of compression loads solely to the Jelly Roll and its casing, achieved by cutting the battery heads. Through this method, distinct mechanical behaviors are identified within the battery force displacement curve: an elastic region, a zone characterized by plastic deformation, and a segment exhibiting densification. By delineating these regions, our study facilitates a comprehensive understanding of the battery’s mechanical response under compression. Two battery models were employed in this study: one representing the battery as a solid volume, and another featuring the jelly roll as a solid volume enclosed by a shell representing the casing. The material utilized was LS Dyna MAT24, chosen for its piecewise characteristics’ definition, and its validation was primarily conducted through the curve fitting method applied to the force–displacement curve, taking in account the three regions of the compression force behavior. This approach not only optimizes computational resources but also offers insights crucial for enhancing the mechanical stability of Li-ion batteries in automotive applications.

Motor Fault Detection and Isolation for Multi-Rotor UAVs Based on External Wrench Estimation and Recurrent Deep Neural Network

AbstractFast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing one or more rotors. We present a motor fault detection and isolation (FDI) method for multi-rotor UAVs based on an external wrench estimator and a recurrent neural network composed of long short-term memory nodes. The proposed approach considers the partial or total motor fault as an external disturbance acting on the UAV. Hence, the devised external wrench estimator trains the network to promptly understand whether the estimated wrench comes from a motor fault (also identifying the motor) or from unmodelled dynamics or external effects (i.e., wind, contacts, etc.). Training and testing have been performed in a simulation environment endowed with a physic engine, considering different UAV models operating under unknown external disturbances and unexpected motor faults. To further assess this approach’s effectiveness, we compare our method’s performance with a classical model-based technique. The collected results demonstrate the effectiveness of the proposed FDI approach.

Visibility and safety of vehicle traffic in the dark (current reality, future projections)

Visibility and safety of vehicle traffic in the dark (current reality, future projections)

СВІТОВІ ТРЕНДИ У АВТОМОБІЛЕБУДІВНІЙ ГАЛУЗІ: УРОКИ ДЛЯ РОЗВИТКУ АВТОМОБІЛЕБУДУВАННЯ УКРАЇНИ В УМОВАХ ЕКОНОМІЧНОЇ ГЛОБАЛІЗАЦІЇ

The article demonstrates that the automotive industry is a significant component of the global economy due to its overall market volume, contribution to GDP, and the number of employed workers. The importance of the automotive industry has prompted an exploration of current global trends in the automotive industry. The trends identified include the development of 'green' technologies in the context of addressing climate change, the rise of 'green' protectionism, and the intensification of economic rivalry between China and Western countries. Other trends encompass the proliferation of autonomous vehicles based on the advancement of artificial intelligence technologies, the integration of cars into the broader network of connected devices within the Internet of Things and cloud technologies, the increased concentration of global value chains, the growth of the sharing economy, urbanization, and the development of urban transport. Furthermore, there is a heightened emphasis on the safety of vehicles, along with regulatory frameworks addressing these requirements. The emergence and development of individual flying devices as alternative modes of transportation are also noteworthy trends. With the aim of assessing the relevance of these trends for Ukraine and their implementation, manifestations of these trends in Ukraine have been evaluated. It has been shown that the most pronounced manifestations include the increased concentration of global value chains and urbanization along with the development of urban transport. To assess the prospects for further development of the examined trends in Ukraine, an evaluation of the readiness of the Ukrainian economy to implement these trends in the automotive industry is presented. The analysis reveals that the Ukrainian economy is most prepared for trends such as the development of 'green' technologies in the context of combating climate change and the growth of the sharing economy. It is shown that the trends in automotive engineering both present opportunities and pose threats to domestic car manufacturers. The composition of these opportunities and threats is outlined. Explored opportunities are mainly associated with the emergence of additional market segments, a stable demand pattern, and the creation of additional economic advantages. The presented threats are mostly linked to changes in demand structure and the insufficient competitiveness of Ukrainian automobile manufacturers in the global market. Factors limiting the opportunities of Ukrainian automotive manufacturers are identified, and the consequences of these factors are demonstrated, ultimately leading to the insufficient competitiveness of Ukrainian producers, an inability to scale production, and difficulties in technical upgrading. The results of the article are of interest for shaping the development of the automotive industry in Ukraine.

Unified Fault-Tolerant Control and Adaptive Velocity Planning for 4WID-4WIS Vehicles under Multi-Fault Scenarios

Four-wheel independent drive and four-wheel independent steering (4WID-4WIS) vehicles provide increased redundancy in fault-tolerant control (FTC) schemes, enhancing heterogeneous fault-tolerant capabilities. This paper addresses the challenge of maintaining vehicle safety and maneuverability in the presence of actuator faults in autonomous vehicles, focusing on 4WID-4WIS systems. A novel unified hierarchical active FTC strategy is proposed to handle various actuator failures. The strategy includes an upper-layer motion controller that determines resultant force requirements based on trajectory tracking errors and a middle-layer allocation system that redistributes tire forces to fault-free actuators using fault information. This study, for the first time, considers multi-fault scenarios involving longitudinal and lateral coupling, calculating FTC boundaries for each fault type. Additionally, a fault tolerance index is introduced for 256 fault scenarios, using singular value decomposition to linearly represent the vehicle attainable force domain. Based on this, an adaptive velocity planning strategy is developed to balance safety and maneuverability under fault conditions. Matlab 2021a/Simulink and Carsim 2019 co-simulation results validate the proposed strategies, demonstrating significant improvements in fault-tolerant performance, particularly in complex and emergency scenarios.

Enhancing Planning for Autonomous Driving via an Iterative Optimization Framework Incorporating Safety-Critical Trajectory Generation

Ensuring the safety of autonomous vehicles (AVs) in complex and high-risk traffic scenarios remains a critical unresolved challenge. Current AV planning methods exhibit limitations in generating robust driving trajectories that effectively avoid collisions, highlighting the urgent need for improved planning strategies to address these issues. This paper introduces a novel iterative optimization framework that incorporates safety-critical trajectory generation to enhance AV planning. The use of the HighD dataset, which is collected using the wide-area remote sensing capabilities of unmanned aerial vehicles (UAVs), is fundamental to the framework. Remote sensing enables large-scale real-time observation of traffic conditions, providing precise data on vehicle dynamics, road structures, and surrounding environments. To generate safety-critical trajectories, the decoder within the conditional variational auto-encoder (CVAE) is innovatively designed through a data-mechanism integration method, ensuring that these trajectories strictly adhere to vehicle kinematic constraints. Furthermore, two parallel CVAEs (Dual-CVAE) are trained collaboratively by a shared objective function to implicitly model the multi-vehicle interactions. Inspired by the concept of “learning to collide”, adversarial optimization is integrated into the Dual-CVAE (Adv. Dual-CVAE), facilitating efficient generation from normal to safety-critical trajectories. Building upon this, these generated trajectories are then incorporated into an iterative optimization framework, significantly enhancing the AV’s planning ability to avoid collisions. This framework decomposes the optimization process into stages, initially addressing normal trajectories and progressively tackling more safety-critical and collision trajectories. Finally, comparative case studies of enhancing AV planning are conducted and the simulation results demonstrate that the proposed method can efficiently enhance AV planning by generating safety-critical trajectories.

Reliability analysis for running safety of vehicle on slab track via an improved second-order fourth-moment approach

The reliability assessment for running safety of vehicle on slab track under temperature action and seismic excitation is challenging due to the extensive computational efforts. This study proposes an improved second-order fourth-moment (ISOFM) approach for the reliability analysis for running safety of vehicle on slab track under temperature action and seismic excitation. The dimensionless limit state function (LSF) for running safety of vehicle on slab track is formulated considering four failure modes. The proposed ISOFM approach is verified by the Monte Carlo simulation (MCS) method. The proposed ISOFM approach greatly reduces the number of computations of nonlinear LSF. The merit of the proposed ISOFM approach lies in significantly enhancing the computation efficiency and precision of reliability considering nonlinear LSF with multi-failure modes. Additionally, the influences of temperature action and seismic excitation on the reliability for running safety of vehicle on slab track are discussed. The reliability for vehicle running safety decreases more significantly with the increment in overall temperature compared to the positive temperature gradient (PTG). The reliability for running safety of vehicle on slab track decreases nonlinearly with the increase of the earthquake intensity.

Hierarchical MPC-based authority allocation strategy for human–machine shared vehicles considering human–machine conflict

The uncertainty of driver behavior is an important factor affecting the safety of human–machine co-driving vehicles. Traditional rule-based models often fail to capture the nonlinear and complex characteristics of human steering behavior. To overcome this, we propose a data network-driven approach utilizing gated recurrent unit (GRU) neural networks to accurately predict driver steering behavior. The GRU-based driver model is integrated with vehicle dynamics to construct a control-oriented driver–vehicle model. Considering that human–machine conflict may cause vehicle instability, an exponential function combining proportional–integral–derivative is proposed to quantify the human–machine conflict level based on steering difference. To reasonably allocate human–computer permissions based on human–machine interaction, a hierarchical authority allocation framework is proposed. The upper layer provides a reference authority allocation via an exponential function, while the lower layer employs a real-time model predictive control (MPC) optimizer to track this reference, ensuring optimal vehicle path tracking and stability. The proposed system’s effectiveness is validated through driver-in-the-loop testing, demonstrating significant improvements in safety and performance. The results show that in the human–machine conflict scenario, the proposed authority allocation strategy can still ensure the path tracking and safety of the vehicle.

EFMF-pillars: 3D object detection based on enhanced features and multi-scale fusion

As unmanned vehicle technology advances rapidly, obstacle recognition and target detection are crucial links, which directly affect the driving safety and efficiency of unmanned vehicles. In response to the inaccurate localization of small targets such as pedestrians in current object detection tasks and the problem of losing local features in the PointPillars, this paper proposes a three-dimensional object detection method based on improved PointPillars. Firstly, addressing the issue of lost spatial and local information in the PointPillars, the feature encoding part of the PointPillars is improved, and a new pillar feature enhancement extraction module, CSM-Module, is proposed. Channel encoding and spatial encoding are introduced in the new pillar feature enhancement extraction module, fully considering the spatial information and local detailed geometric information of each pillar, thereby enhancing the feature representation capability of each pillar. Secondly, based on the fusion of CSPDarknet and SENet, a new backbone network CSE-Net is designed in this paper, enabling the extraction of rich contextual semantic information and multi-scale global features, thereby enhancing the feature extraction capability. Our method achieves higher detection accuracy when validated on the KITTI dataset. Compared to the original network, the improved algorithm’s average detection accuracy is increased by 3.42%, it shows that the method is reasonable and valuable.

Segmentation-assisted classification model with convolutional neural network for weld defect detection

Detecting weld defects in battery trays is crucial for the safety of new energy vehicles. Existing methods for weld surface defect detection, relying on traditional computer vision algorithms and convolutional neural networks with substantial image-level labeled data, face challenges in accurately identifying small defects, especially with limited samples. To address these issues, we developed an innovative Segmentation-Assisted Classification with Convolutional Neural Networks (SACNN) model. SACNN integrates a common feature extraction subnet, a segmentation subnet enhanced by a multi-scale feature fusion module, and a classification subnet specifically designed for precise defect detection. A joint loss function co-trains the segmentation and classification subnets using both image-level and pixel-level labels, enhancing the model’s ability to accurately detect small defect regions. Our model demonstrates notable improvement, achieving accuracy gains ranging from 2% to 18% compared to existing state-of-the-art methods, with an overall accuracy of 94.09% on an industrial dataset of battery tray welds. To further evaluate the generalization capability of our model, we evaluated it on the publicly available Magnetic Tile dataset, achieving state-of-the-art results in this challenging context. Additionally, we conducted comprehensive ablation studies to validate the contribution of each component in our approach and utilized visualization techniques to enhance the interpretability of our model. These advancements represent a significant contribution to the state of the art in aluminum alloy weld defect detection.

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

Enhancement of photoresponse for InGaAs infrared photodetectors using plasmonic WO3-x/CsyWO3-xnanocrystals.

Fast and accurate detection of light in the near-infrared (NIR) spectral range plays a crucial role in modern society, from alleviating speed and capacity bottlenecks in optical communications to enhancing the control and safety of autonomous vehicles through NIR imaging systems. Several technological platforms are currently under investigation to improve NIR photodetection, aiming to surpass the performance of established III-V semiconductor p-i-n (PIN) junction technology. These platforms include in situ-grown inorganic nanocrystals and nanowire arrays, as well as hybrid organic-inorganic materials such as graphene-perovskite heterostructures. However, challenges remain in nanocrystal and nanowire growth, large-area fabrication of high-quality 2D materials, and the fabrication of devices for practical applications. Here, we explore the potential for tailored semiconductor nanocrystals to enhance the responsivity of planar metal-semiconductor-metal (MSM) photodetectors. MSM technology offers ease of fabrication and fast response times compared to PIN detectors. We observe enhancement of the optical-to-electric conversion efficiency by up to a factor of ~2.5 through the application of plasmonically-active semiconductor nanorods and nanocrystals. We present a protocol for synthesizing and rapidly testing the performance of non-stoichiometric tungsten oxide (WO3-x) nanorods and cesium-doped tungsten oxide (CsyWO3-x) hexagonal nanoprisms prepared in colloidal suspensions and drop-cast onto photodetector surfaces. The results demonstrate the potential for a cost-effective and scalable method exploiting tailored nanocrystals to improve the performance of NIR optoelectronic devices.

Multimodal vehicle trajectory prediction and integrated threat assessment algorithm based on adaptive driving intention

As autonomous driving and connected communication technologies advance swiftly, vehicle trajectory prediction has become increasingly significant. The motion of a vehicle is contingent not only on its historical trajectory but is also subject to the influence of surrounding vehicles, thereby exhibiting intricate social and temporal interdependencies. Furthermore, the inherent randomness and uncertainty in driver behavior render vehicle trajectory prediction inherently multimodal, a factor that is frequently neglected in current research. Against this backdrop, a multimodal vehicle trajectory prediction (MTP) model based on an encoder-decoder architecture is proposed to hierarchically extract historical features of vehicles. The model consists of five key components: temporal feature encoder module, spatial interaction module, spatial-temporal dependence module, driving intention fusion module and multimodal trajectory output module. Experiments on the NGSIM dataset show that the predictive performance of the model has been improved to varying degrees, especially at 3–5 s, where the improvement is more significant. Compared with state-of-the-art models, the Root Mean Square Error (RMSE) error at 5 s time horizon is 3.38 m on NGSIM dataset, which represents a 25 % improvement. To measure the safety of predicted trajectories, we propose a comprehensive threat assessment model that combines collision time (TTC), headway (TH) and time to lateral collision (TLC) metrics based on safe distance theory. This model not only evaluates the longitudinal collision threat in the following state, but also evaluates the lateral collision threat during driving maneuvers in multi lane scenarios, thereby comprehensively improving the safety of vehicle driving. This research also offers new perspectives and insights for the development of autonomous driving.

Effect of exo-fiber ring, volume fraction and moisture on the mechanical properties of the 3D printed onyx-carbon fiber composite: an experimental study

Abstract The present study investigates the potential of onyx-carbon fiber composites for automotive applications, like bumpers focusing on mechanical properties and water absorption behaviour. The study involves tensile, compression, and flexural tests, alongside water absorption analysis, to determine the optimal composite configuration. Results indicate that higher carbon fiber volume fractions (10%–15%) and multiple concentric layers (exo-fibers) (3–4) significantly enhance the mechanical performance. The configuration of three concentric layers with 15% carbon fiber demonstrates superior tensile strength, compressive strength, and flexural strength, along with minimal long-term water absorption. This configuration offers a balanced combination of high strength, energy absorption, and durability, making it ideal for enhancing vehicle safety and performance. Future research should focus on optimizing manufacturing techniques and exploring smart material integration to further improve the adaptive properties and damage detection capabilities of these composites, paving the way for next-generation automotive technologies.

A novel bionic lotus leaf channel liquid cooling plate for enhanced thermal management of lithium-ion batteries

Battery Thermal Management Systems (BTMS) are pivotal in safeguarding the safety, efficiency, and lifespan of electric vehicles. In this study, we propose a bionic lotus leaf (BLL) channel liquid-cooled plate to mitigate temperature accumulation issues observed during lithium-ion battery (LIB) discharge. The primary objective is to enhance heat dissipation and achieve uniform temperature distribution within LIBs. We investigated the impact of different interior configurations and mass flow rates on individual lithium-ion battery (LIB) temperatures using single-variable analysis. Our findings indicate that higher inlet mass flow rates effectively lower the temperature increase in individual LIBs, albeit at the cost of inducing a significant pressure drop.The optimal parameter combination is determined through orthogonal analysis. Specifically, the channel angle (α) and inlet mass flow rate (M) are found to significantly influence the maximum temperature (Tmax) of LIBs. The number of channels (N) is optimized to enhance temperature distribution and reduce maximum temperature variation (ΔTmax) within LIBs. Furthermore, adjusting the channel width (D) substantially mitigated pressure drop (ΔP) in the BLL channel liquid cooling plate, thereby improving hydraulic pump efficiency. The identified optimal combination (M = 1.0 g s-1, N = 4, D = 2.5 mm, α = 90°) ensures that Tmax remains below 29.732 °C even during a 5C discharge, a critical requirement. Compared to a traditional serpentine channel with equivalent heat transfer area and inlet mass flow, the optimized BLL channel showed a decrease in Tmax by 0.776 °C, ΔTmax reduction by 2.445 °C, and ΔP reduced to only 43.44 % of that in the serpentine channel. The BLL channel proposed in this research can serve as a valuable reference for BTMS.

KPTr: Key point transformer for LiDAR-based 3D object detection

Accurate 3D environmental perception is crucial for ensuring the safety of autonomous vehicles. However, many existing point-based 3D object detection methods rely only on independent point features to extract spatial information and ignore the dependencies between points. Meanwhile, these methods usually use max-pooling operations to aggregate neighboring point features without considering the stability of local geometric information. To address these issues, this paper proposes a novel two-stage LiDAR-based key point transformer that consists of a region proposal network module and a region-based transformer module to enhance 3D detection performance. Firstly, unprocessed raw point clouds are taken as the input of the model to preserve detailed spatial structural information. Secondly, in the region proposal network an enhanced PointNet++ is used to segment foreground points and generate high-quality first-stage proposals. Then, the representation of proposals is improved by the region-based transformer module, which constructs long-range key point cloud relationship features within the regions of interest and diffuses spatial information into the feature channel layers. Additionally experiments on the KITTI dataset show that detection performance of the proposed method surpasses most mainstream 3D object detection methods and achieves a detection speed of 14.28 Hz, striking a good balance between detection accuracy and speed. Finally, the proposed method is deployed on real vehicle hardware platforms for online detection, thereby proving its industrial value.

Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles

The integration of IoT systems into automotive vehicles has raised concerns associated with intrusion detection within these systems. Vehicles equipped with a controller area network (CAN) control several systems within a vehicle where disruptions in function can lead to significant malfunctions, injuries, and even loss of life. Detecting disruption is a primary concern as vehicles move to higher degrees of autonomy and the possibility of self-driving is explored. Tackling cyber-security challenges within CAN is essential to improve vehicle and road safety. Standard differences between different manufacturers make the implementation of a discreet system difficult; therefore, data-driven techniques are needed to tackle the ever-evolving landscape of cyber security within the automotive field. This paper examines the possibility of using machine learning classifiers to identify cyber assaults in CAN systems. To achieve applicability, we cover two classifiers: extreme gradient boost and K-nearest neighbor algorithms. However, as their performance hinges on proper parameter selection, a modified metaheuristic optimizer is introduced as well to tackle parameter optimization. The proposed approach is tested on a publicly available dataset with the best-performing models exceeding 89% accuracy. Optimizer outcomes have undergone rigorous statistical analysis, and the best-performing models were subjected to analysis using explainable artificial intelligence techniques to determine feature impacts on the best-performing model.