An Improved Autonomous Emergency Braking Algorithm for AGVs: Enhancing Operational Smoothness Through Multi-Stage Deceleration.

Autonomous emergency braking (AEB) is one of the safety technologies of the integrated electronic brake (IEB) system and is critical to avoiding or mitigating vehicular collisions. In addition, a motor is a significant component of the IEB system for its actuation, and it greatly affects this system’s performance under AEB. Notably, the determination of the IEB system’s motor parameters without considering its performance under AEB will not satisfy its requirements, especially due to this system’s hydraulic and electric complexity. In addition, the IEB system motor’s dynamic characteristics have a significant impact on the AEB performance. They are also closely related to this motor’s parameters, such as flux-linkage, inductance, and resistance. Therefore, this research performs the IEB system’s motor parametric design corresponding to this system’s necessary performance under AEB, using a coupled simulation model composed of this system’s motor and hydraulic subsystems. First, the IEB system’s motor and hydraulic subsystems are modeled in MATLAB/Simulink and AMESim, respectively, and the coupled simulation of these subsystems is performed in Simulink using their models. Second, the respective ranges of the IEB system’s motor parameters corresponding to this system’s necessary performance under AEB are determined from the above-mentioned coupled simulation, and their corresponding motor is designed using a finite element analysis in ANSYS Maxwell. Third, the IEB system’s fabricated, parametric-designed motor is tested for standalone performance. Finally, the IEB system incorporated with the said parametric-designed motor is experimentally verified of its performance under AEB. Meanwhile, the respective simulated and experimental performances of the IEB system under AEB and its parametric-designed motor agree well. This agreement indicates that the IEB system’s motor parametric design presented in this research, based on this system’s coupled simulation model and finite element analysis on this model, is valid.

ABSTRACTIn the context of industrial automation, optimising automated guided vehicle (AGV) trajectories is crucial for enhancing operational efficiency and safety. They must travel in crowded work areas and cross narrow corridors with strict safety and time requirements. Bio‐inspired optimization algorithms have emerged as a promising approach to deal with complex optimization scenarios. Thus, this paper explores the ability of three novel bio‐inspired algorithms: the Bat Algorithm (BA), the Whale Optimization Algorithm (WOA) and the Gazelle Optimization Algorithm (GOA); to optimise the AGV path planning in complex environments. To do it, a new optimization strategy is described: the AGV trajectory is based on clothoid curves and a specialised piece‐wise fitness function which prioritises safety and efficiency is designed. Simulation experiments were conducted across different occupancy maps to evaluate the performance of each algorithm. WOA demonstrates faster optimization providing suitable safety solutions 4 times faster than GOA. Meanwhile, GOA gives solutions with better safety metrics but demands more computational time. The study highlights the potential of bio‐inspired approaches for AGV trajectory optimisation and suggests avenues for future research, including hybrid algorithm development.

In the case of knowing the environmental information of static obstacles, in order to improve the path of AGV (automatic guided vehicle) in logistics system, the paper uses a method of path planning which based on modified potential field. The potential field is more convenient and efficient than the intelligent method, and it's one of many path planning methods. The calculated amount of improved algorithm is little, it's easier to meet real-time requirement and relatively simple in mathematical description, resulting in relatively smooth and safe path. Improved potential field improves the operation efficiency and safety of the system in actual system applications. The analysis of the typical examples verifies the validity of this method.

In this study, a control algorithm that improves the brake performance of the AEB (Autonomous emergency braking) system on a ramp was proposed. Because the conventional AEB system-applied vehicle is set up on the basis of flat road conditions, it has limitations in its braking permission time and its collision avoidance performance, which do not consider the effects of a gradient in a ramp environment. To improve braking performance on a ramp, the development of an AEB algorithm that considers the gradient is required. A new braking permission time was proposed by calculating a minimum deceleration distance and TTC (Time to collision) confirmation time required to brake on the basis of force received by a vehicle on a ramp. It was confirmed that the AEB algorithm proposed in this paper improved the collision-avoidance performance compared with the conventional AEB algorithm.

The deployment of heterogeneous Automated Guided Vehicles (AGVs) in smart manufacturing requires control strategies that can accommodate distinct actuation characteristics and constraints. This paper proposes a Multi-Factor Coupled Parameter-Adaptive Model Predictive Control (MFCP-AMPC) framework. Unlike conventional approaches requiring vehicle-specific tuning, this framework unifies differential-drive, dual-steer, and mecanum-wheel platforms under a single parameter-varying state-space model that respects the specific actuation limits of each topology. A key contribution is the multi-factor coupling mechanism that dynamically adjusts the prediction horizon and weighting matrices based on path curvature, vehicle speed, and tracking error. Experiments on industrial AGV prototypes demonstrate that the framework achieves robust tracking precision under varying payloads. Crucially, by acknowledging physical limits, the framework achieves strict millimeter-level accuracy (RMSE < 7 mm) in quasi-static low-speed complex maneuvers (v≤0.3 m/s), and maintains highly competitive industrial precision (RMSE ≈ 15∼25 mm) under aggressive high-speed tracking (v≥1.0 m/s). Crucially, the proposed method significantly improves the control input smoothness (Smoothness Index > 0.75), thereby reducing mechanical wear and preventing actuator saturation. Real-time validation (12 ms average solve time on an Intel i7 IPC) confirms its suitability for resource-constrained industrial controllers.

ABSTRACTObjective: Autonomous emergency braking (AEB) is a safety system that detects imminent forward collisions and reacts by slowing down the host vehicle without any action from the driver. AEB effectiveness in avoiding and mitigating real-world crashes has recently been demonstrated. Research suggests that a translation of AEB to powered 2-wheelers could also be beneficial. Previous studies have estimated the effects of a motorcycle AEB system (MAEB) via computer simulations. Though effects of MAEB were computed for motorcycle crashes derived from in-depth crash investigation, there may be some inaccuracies due to limitations of postcrash investigation (e.g., inaccuracies in preimpact velocity of the motorcycle). Furthermore, ideal MAEB technology was assumed, which may lead to overestimation of the benefits. This study sought to evaluate the sensitivity of the simulations to variations in reconstructed crash cases and the capacity of the MAEB system in order to provide a more robust estimation of MAEB effects.Methods: First, a comprehensive classification of accidents was used to identify scenarios in which MAEB was likely to apply, and representative crash cases from those available for this study were populated for each crash scenario. Second, 100 variant cases were generated by randomly varying a set of simulation parameters with given normal distributions around the baseline values. Variants reflected uncertainties in the original data. Third, the effects of MAEB were estimated in terms of the difference in the impact speed of the host motorcycle with and without the system via computer simulations of each variant case. Simulations were repeated assuming both an idealized and a realistic MAEB system. For each crash case, the results in the baseline case and in the variants were compared.A total of 36 crash cases representing 11 common crash scenarios were selected from 3 Australian in-depth data sets: 12 cases from New South Wales, 13 cases from Victoria, and 11 cases from South Australia.Results: The reduction in impact speed elicited by MAEB in the baseline cases ranged from 2.8 to 10.0 km/h. The baseline cases over- or underestimated the mean impact speed reduction of the variant cases by up to 20%. Constraints imposed by simulating more realistic capabilities for an MAEB system produced a decrease in the estimated impact speed reduction of up to 14% (mean 5%) compared to an idealized system.Conclusions: The small difference between the baseline and variant case results demonstrates that the potential effects of MAEB computed from the cases described in in-depth crash reports are typically a good approximation, despite limitations of postcrash investigation. Furthermore, given that MAEB intervenes very close to the point of impact, limitations of the currently available technologies were not found to have a dramatic influence on the effects of the system.

Evaluating impacts of different longitudinal driver assistance systems on reducing multi-vehicle rear-end crashes during small-scale inclement weather

To avoid collision in emergency situations, a variable time headway (VTH) autonomous emergency braking (AEB) control algorithm based on model predictive control (MPC) theory is proposed. The VTH based safety distance model is designed to avoid this kind of interference. Taking into account of collision avoiding, driver characteristics and ride comfort factors, the AEB controller is designed. The distance between the two vehicles, the relative velocity, the velocity and acceleration of the ego vehicle are taken as the variables to construct the state equation. The ego vehicle can slow down or take an emergency braking control. Several simulation conditions are conducted to validate the proposed AEB controller by the testing standards for AEB system. Results indicate that the proposed VTH based AEB controller can meet the requirements of AEB system.

With the increasing consumer focus on automotive safety, Autonomous Emergency Braking (AEB) systems, recognized as effective active safety technologies for collision avoidance and the mitigation of collision-related injuries, are gaining wider application in the automotive industry. To address the issues of the insufficient working reliability of AEB systems and their unsatisfactory level of accordance with the psychological expectations of drivers, this study proposes an optimized second-order Time to Collision (TTC) safety time algorithm based on the motion state of the preceding vehicle. Additionally, the study introduces a safety distance algorithm derived from an analysis of the braking process of the main vehicle. The safety time algorithm focusing on comfort and the safety distance algorithm focusing on safety are effectively integrated in the time domain and the space domain to obtain the safety time–safety distance fusion algorithm. A MATLAB/Simulink–Carsim joint simulation platform has been established to validate the AEB control strategy in terms of safety, comfort, and system responsiveness. The simulation results show that the proposed safety time–safety distance fusion algorithm consistently achieves complete collision avoidance, indicating a higher safety level for the AEB system. Furthermore, the application of active hierarchical braking minimizes the distance error, at under 0.37 m, which meets psychological expectations of drivers and improves the comfort of the AEB system.

Research on safety of the intended functionality of automobile AEB perception system in typical dangerous scenarios of two-wheelers

To address inefficiency, high safety risks, and excessive labor costs in pallet loading within the tobacco logistics industry, this paper proposes an intelligent loading system for automated guided vehicles (AGVs) based on simultaneous localization and mapping (SLAM) and multi-sensor fusion. The system integrates LiDAR, depth cameras, and inertial measurement units (IMUs). Through data fusion and adaptive localization algorithms, it achieves high-precision mapping, positioning, and dynamic obstacle avoidance. Experimental results show that under conditions of a 10 cm vehicle offset and a 3°angular deviation, the system successfully navigates with a 100% success rate. Loading accuracy is maintained within 0.5– 1.7 cm, and the average loading time is reduced to 1.5 minutes, improving efficiency by 50% compared to manual operation. Moreover, the system demonstrates strong robustness in dynamic environments, effectively handling the challenges of complex warehouse scenarios. The proposed approach enhances automation and safety in logistics operations and shows great potential for application in the tobacco industry and beyond.

Analysis of the minimum swerving distance for the development of a motorcycle autonomous braking system

The combined effect of vehicle frontal design, speed reduction, autonomous emergency braking and helmet use in reducing real life bicycle injuries

This study introduces a method that allows the generation and safety evaluation of a scenario catalog derived from potential car-pedestrian conflict situations. It is based on open-source software components and uses the road layout standard OpenDRIVE to derive participants’ motion profiles with the support of available accident data. The method was implemented upon the open-source framework openPASS and can simulate results for different active safety system setups and facilitates the prediction of system capabilities to decrease the relative impact velocities and collision configurations such as the point of impact. A demonstration case was performed where the scenario catalog was derived and used to evaluate pedestrian collisions with and without a generic autonomous emergency braking (AEB) system. The AEB system aims to intervene in the event of an impending collision and might affect the outcome of a baseline scenario. The study indicated a change in the collision configuration and identified conflict situations less affected by the system. A particularly interesting finding was that some scenarios even led to a higher number of collisions (at lower impact) for the AEB intervention in comparison to the baseline cases.

Autonomous vehicle (AV) is expected to be the ultimate solution for traffic safety, while autonomous emergency braking (AEB), as a crucial and fundamental active safety function of AV, has excellent potential for reducing fatalities and improving road safety. Although AV has the ability to cope with harsh conditions, it is supposed to be tested fully, systematically, and rigorously before it is officially commercialized. This study developed a novel indoor AV-in-the-loop (AVIL) simulation platform based on Client-Server (C/S) architecture for real full-scale AV testing. The proposed AVIL simulation platform consists of three parts: physical hardware components, software components, and various electrical interfaces that ensure the bidirectional virtual reality (VR) interaction. To validate the functionality and performance of the platform, this paper then adopted the Udwadia–Kalaba (U-K) approach to build the AEB system based on a typical driving situation due to the explicitness and simplicity of the U-K approach. Further, a group of real road-based experiments and AVIL-based experiments were conducted. The experimental results showed that the testing data obtained from the proposed AVIL platform have a high similarity to those of the real road tests, which means that the proposed AVIL platform is capable of simulating the AV running condition when it performs linear emergency braking on the road, thus confirming the feasibility and effectiveness of the AVIL platform for AV AEB testing. Simultaneously, the testing time and repeatability of the latter performed better. The findings of this study provide a new safe, effective, and fast solution to AV testing, and the practicability of this method has been verified.