Vehicle Maneuvers Research Articles

Application of Artificial Intelligence Technology in New Energy Vehicles

Due to environmental pollution caused by excessive exploitation of world resources, countries are now advocating for green travel, which has led to the rapid development of the new energy vehicle industry. Compared to traditional cars, the safety, maneuverability, and convenience of new energy vehicles have made the application of artificial intelligence technology increasingly widespread in this field. Therefore, this article provides a systematic review and analysis of the application of artificial intelligence technology in new energy vehicles. Firstly, the article outlined the main application scenarios of artificial intelligence in the field of new energy vehicles, including autonomous driving, energy management optimization, vehicle intelligent interconnection, and predictive maintenance. On this basis, this paper analyzed the role of artificial intelligence technology in improving the performance, safety, and reliability of new energy vehicles, as well as the technological challenges and development trends it faces. For example, issues such as data privacy protection and real-time performance need to be further addressed. In the future, artificial intelligence and new energy vehicles will further integrate, making important contributions to achieving intelligent, green, and safe future travel.

Predicting the Future Signalization of Traffic-Actuated Signals Using Extreme Gradient Boosting

Stops, braking, and acceleration maneuvers at traffic signals cause high fuel consumption and emission levels. Green light optimized speed advisory (GLOSA) can help to reduce a proportion of such unnecessary vehicle maneuvers. For this, GLOSA systems require reliable switching time estimations as input. However, estimating the switching times of traffic-actuated signals can be challenging. The signalization of traffic-actuated lights is adjusted to match the current traffic. Based on green-time requests, the status of signals can change with almost no lead time. This paper presents an approach for predicting the signalization of traffic-actuated signals. We exploit the limited nature of the space for switched signal state combinations at intersections, and express combinations of motorized traffic-related signal states as one feature to depict the motorized traffic signalization state (MTSSt) at an intersection. Predicting MTSSt-switches allows later determination of the switching times of individual signals. To conduct the predictions, the machine learning method “extreme gradient boosting” was used. A three-step methodology—data preparation, tuning the prediction procedure, and testing the approach—was applied and evaluated on the historical data of four traffic-actuated signalized intersections. The results showed that within a period of the next 30 s, signal changes were predicted with an overall precision and sensitivity of about 95% and an average mean absolute error of less than 1.1 s. In this period, the sequence of switched signals was predicted accurately to the second, without any deviation, in, on average, over 82% of cases.

Responses of Vehicular Occupants During Emergency Braking and Aggressive Lane-Change Maneuvers.

To validate active human body models for investigating occupant safety in autonomous cars, it is crucial to comprehend the responses of vehicle occupants during evasive maneuvers. This study sought to quantify the behavior of midsize male and small female passenger seat occupants in both upright and reclined postures during three types of vehicle maneuvers. Volunteer tests were conducted using a minivan, where vehicle kinematics were measured with a DGPS sensor and occupant kinematics were captured with a stereo-vision motion capture system. Seatbelt loads, belt pull-out, and footrest reaction forces were also documented. The interior of the vehicle was 3D-scanned for modeling purposes. Results indicated that seatback angles significantly affected occupant kinematics, with small female volunteers displaying reduced head and torso movements, except during emergency braking with a upright posture seatback. Lane-change maneuvers revealed that maximum lateral head excursions varied depending on the maneuver's direction. The study concluded that seatback angles were crucial in determining the extent of occupant movement, with notable variations in head and torso excursions observed. The collected data assist in understanding occupant behavior during evasive maneuvers and contribute to the validation of human body models, offering essential insights for enhancing safety systems in autonomous vehicles.

In situ optical measurement of particles in sediment plumes generated by a pre-prototype polymetallic nodule collector

This study presents in situ, high-resolution optical measurements of particle size distributions (PSD) within sediment plumes generated by a pre-prototype deep seabed nodule collector vehicle operating in the abyssal Pacific Ocean. These measurements were obtained using a cutting-edge instrument, the LISST-RTSSV sensor. The data collected in situ reveal marked differences compared to previously reported laboratory-based, ex situ measurements. The grain size and other key particle shape characteristics are found to be dependent on multiple factors, including the collector vehicle maneuvers, the time elapsed following sediment discharge, and the complex hydrodynamic processes that generate the sediment in suspension. Significantly, the PSD from a highly complex succession of straight-line maneuvers converges to that of the canonical case of a simple straight-line driving maneuver within a timescale of ten minutes. Our results underscore the importance of parameterizing sediment plume transport models based on well-informed, comprehensive PSDs of detrained suspended sediment measured in situ at adequate timescales and in regions no longer strongly influenced by active and complex hydrodynamic processes.

Real-Time Embedded Control of Vehicle Dynamics Using ESP32: A Discrete Nonlinear Approach

This article explores the application of the Espressif ESP32 System-On-Chip (SoC) for managing vehicle dynamics through real-time digital proportional–integral (PI-like) control. We present the development of advanced driving assistance algorithms for Active Front Steering (AFS) and Rear Torque Vectoring (RTV) on this cost-effective, commercially available embedded system. Using digital PI-like control algorithms designed for AFS and RTV, the primary ESP32 board receives and processes steering signals, executing a discrete-time control model of the vehicle dynamic to enable dynamic adjustments to steering and torque. To enhance simulation realism, a secondary ESP32 is employed to generate the steering signal, effectively mimicking a steer-by-wire system via its analog output ports. This configuration facilitates the simulation and evaluation of control algorithms in a realistic test environment, ensuring enhanced vehicle dynamic stability and maneuverability under various conditions. Additionally, simulations are conducted using MATLAB 2023a and CarSim 2017.1 to compare the efficacy and benefits of the implementation. Our objective is to establish a platform for evaluating discrete controllers capable of real-time vehicle operation. This methodology accelerates and reduces the cost of improving vehicle system stability and responsiveness, enabling the immediate verification and fine-tuning of control parameters as needed.

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.

Control of the von Kármán vortex street with focusing and vectoring of jet using synthetic jet array

In the present study, a novel flow control technique based on jet focusing and vectoring from a synthetic jet array (SJA) for controlling the wake of a bluff body is proposed and demonstrated. A numerical investigation into the flow past a square cylinder modified by the SJA has been carried out at a free stream Reynolds Number of 100. The SJA consists of four independently controlled synthetic jet actuators operating at a peak velocity of eight times the free stream and fifteen times the natural vortex shedding frequency of the square cylinder. The SJA is operated in two different regimes; a focusing regime involving phase delay (Δφ) with non-linear variation between the actuators and a vectoring regime with a linear phase delay without changing the geometric or operating parameters of the SJA. It has been found that jet focusing is able to reduce the coefficient of drag by as much as 43% for Δφ=90°. Focusing is also observed to reduce the fluctuations in the wake velocity with the maximum reduction in fluctuations also corresponding to Δφ=90°. Jet vectoring is able to deflect the von Kármán vortex street in a singular direction along with shifting of the front stagnation point with maximum deflection for Δφ=60°. Furthermore, vectoring leads to an asymmetry in the wake velocity field with the shifting of the velocity deficit region in the direction of the vectoring along with an asymmetry in the wake velocity fluctuations. This novel approach toward synthetic jet induced active flow control allows for greater manipulation of the flow field characteristics of bluff bodies than present methods with applications in areas of underwater and micro air vehicle maneuvering, automobile, and building aerodynamics among others.

Numerical Investigation of the Fuel Tank Sloshing Condition of a Commercial Vehicle

This study investigates the impact of baffles on fuel sloshing behavior within truck fuel tanks using numerical simulations. The volume of the fluid multiphase model is employed to analyze the flow dynamics of 25% diesel fuel in a 250 L tank, modeled in a 3D domain. Two configurations were compared: a tank with baffles and one without. The primary focus is to analyze fuel distribution within the intake port region during vehicle acceleration and deceleration maneuvers. The simulated scenario mimics a realistic driving situation. The vehicle accelerates from 0 km/h to 60 km/h over 10 s, followed by a 3-s braking period to reach a complete stop (0 km/h) at the 13-s mark. The simulation then observes the fuel behavior within the tank for an addi-tional 7 s while the vehicle remains stationary. Results reveal significant differences in fuel behavior between baffled and unbaffled tanks. In the absence of baffles, the sloshing motion is substantial, leading to a complete depletion of fuel in the intake port region for a duration of 3 s during both the acceleration and deceleration phases (between 10 and 13 s). Compared to a standard tank, the presence of baffles significantly reduced the sloshing amplitude by approximately 70%. Furthermore, baffles led to a 50% decrease in pressure variations on the tank walls. Temporary fuel starvation can negatively impact engine performance and combustion efficiency. Conversely, the presence of baffles within the tank effectively mitigates sloshing and ensures continuous fuel presence at the intake port the entire simulation. This suggests that baffles play a crucial role in maintaining a stable and consistent fuel supply to the engine, even during dynamic vehicle maneuvers.

Quantification of the Vertical Load Applied to the Pavement during Cornering Maneuver of a Battery Electric Commercial Vehicle

Quantification of the Vertical Load Applied to the Pavement during Cornering Maneuver of a Battery Electric Commercial Vehicle

Distributed-observer-based adaptive fault tolerant formation maneuver for autonomous surface vehicles under stochastic cyber-attack

This paper investigates the distributed adaptive fault-tolerant formation maneuver control strategy for multiple autonomous surface vehicles (ASVs) systems subject to stochastic cyber-attack and actuator failures. Existing works have provided solutions against continuous attacks on communication channels while ignoring the increasingly prevalent intermittent attacks. Compared to the former, the latter are more challenging to detect, having been insufficiently described and addressed. Motivated by this observation, this article is concerned with the stochastic intermittent injection attacks, which are modeled as stochastic impulsive sequences, characterized by stochastic injection instant and intensity, leading to unpredictable changes in the state information transmitted by the neighbors. Taking this dilemma into consideration, a novel distributed observer is proposed, capable of effectively observing the target formation even in the presence of stochastic intermittent injection attacks. Meanwhile, a large feedback gain is incorporated into the observer to reduce the impact of attacks on system stability. A linear sliding manifold, together with the adaptive algorithm, is utilized to construct a fault tolerant control architecture for each follower ASV without employing model parameters which can confront the embarrassing scenario of actuator failures. Eventually, theoretical deduction and numerical simulations are conducted to confirm the feasibility of the proposed collaborative control scheme.

Learning-based hierarchical cooperative eco-driving with traffic flow prediction for hybrid electric vehicles

The integration of autonomous driving and hybrid electric vehicle technologies presents a promising solution for achieving environmental sustainability. This paper introduces an innovative energy-efficient driving strategy for hybrid electric vehicles that incorporates real-time traffic flow prediction. The study delves into the impact of both lateral and longitudinal vehicle maneuvers on energy consumption within dynamic traffic environments, offering novel insights into optimizing energy utilization. Firstly, a multi-lane traffic flow state rolling predictor is constructed based on the Hankel dynamic mode decomposition algorithm. Subsequently, a vehicle longitudinal and lateral coordinated control strategy is established by integrating the prioritized experience replay double deep Q-network algorithm. Finally, a novel energy management strategy is proposed that leverages Simulink dynamic model and the deep deterministic policy gradient algorithm to address the vehicle dynamic decision-making planning results. Within a hierarchical cooperative optimization framework, this research comprehensively considers safety, comfort, traffic efficiency, and fuel economy. By introducing a novel hierarchical collaborative ecological driving framework, we have achieved a substantial improvement in environmental sustainability, with traffic efficiency increasing by 10.27%-14.41% and fuel economy rising by 9.44%-10.47%. Hardware-in-the-loop validation has confirmed the proposed approach’s real-time capabilities and promising practical applications.

A high maneuvering motion strategy and stable control method for tandem twin-rotor aerial-aquatic vehicles near the water surface

The maneuverability and stealth of aerial-aquatic vehicles (AAVs) is of significant importance for future integrated air-sea combat missions. To improve the maneuverability and stealth of AAVs near the water surface, this paper proposed a high-maneuverability skipping motion strategy for the tandem twin-rotor AAV, inspired by the motion behavior of the flying fish to avoid aquatic and aerial predators near the water surface. The novel tandem twin-rotor AAV was employed as the research subject and a strategy-based ADRC control method for validation, comparing it with a strategy-based PID control method. The results indicate that both control methods enable the designed AAV to achieve high stealth and maneuverability near the water surface with robust control stability. The strategy-based ADRC control method exhibits a certain advantage in controlling height, pitch angle, and reducing impact force. This motion strategy will offer an inspiring approach for the practical application of AAVs to some extent.

Lane-changing and overtaking trajectory planning for autonomous vehicles with multi-performance optimization considering static and dynamic obstacles

Affected by the complex traffic environment, lane-changing and overtaking have become daily driving operations of autonomous vehicles, and providing a drivable trajectory is one of the critical tasks of planning processes. To this end, this paper aims to propose an optimization-algorithm-based double quintic polynomial trajectory planning model considering static and dynamic obstacles for lane-changing and overtaking maneuvers of the autonomous vehicle. Firstly, an improved double quintic polynomial planning model considering different motion states and sizes of obstacles is constructed by introducing the lane change transition state to ensure the autonomous vehicle’s driving safety. Secondly, a multi-objective performance function considering various influencing factors is established to improve the driving performances of the autonomous vehicle during lane-changing and overtaking. Finally, a particle swarm optimization (PSO) algorithm is used to optimize parameters of the proposed planning model, such as the lane change time, transition speed, and longitudinal displacement, to generate a driveability trajectory that meets the driving safety, comfort, stability, and low emission requirements of the autonomous vehicle during lane-changing and overtaking. The effectiveness and advantages of the proposed planning model are verified by comparing it with several existing planning models under different driving conditions.

Using Reinforcement Learning to Develop a Novel Gait for a Bio-Robotic California Sea Lion.

While researchers have made notable progress in bio-inspired swimming robot development, a persistent challenge lies in creating propulsive gaits tailored to these robotic systems. The California sea lion achieves its robust swimming abilities through a careful coordination of foreflippers and body segments. In this paper, reinforcement learning (RL) was used to develop a novel sea lion foreflipper gait for a bio-robotic swimmer using a numerically modelled computational representation of the robot. This model integration enabled reinforcement learning to develop desired swimming gaits in the challenging underwater domain. The novel RL gait outperformed the characteristic sea lion foreflipper gait in the simulated underwater domain. When applied to the real-world robot, the RL constructed novel gait performed as well as or better than the characteristic sea lion gait in many factors. This work shows the potential for using complimentary bio-robotic and numerical models with reinforcement learning to enable the development of effective gaits and maneuvers for underwater swimming vehicles.

Diagnostic methodology for vehicles equipped with antilock braking system and electronic stability control

Currently, several automotive companies are joining forces through the analysis and development of active safety systems for hazard avoidance and mitigation; from which according to the current regulations in the European Union the Anti-lock Braking System (ABS) and the Electronic Stability Control (ESC) outstand as mandatory for each new vehicle. The above has motivated the study of these two safety systems. Then, this research examines the ABS and ESC systems through a proposed diagnostic methodology, which identifies the main effect of these systems on the vehicle dynamics. To achieve this goal a software was developed, looking for processing the data acquired during the development of some specific vehicle maneuvers. The diagnostic results expose: (1) an indicator about the ABS/ESC systems performance; and (2) the impact of these systems on the vehicle dynamics; also, some of the results here obtained can also be taken as reference for the Brake Assistant System and the Anti-Roll Control analysis.

Framework for Numerical 6DOF Simulation with Focus on a Wing Deforming UAV in Perch Landing

The perch landing maneuver of a wing-deforming unmanned aerial vehicle (UAV) was investigated through a framework that uses the free, open-source OpenFOAM with 6 degrees of freedom (6DOF) simulations. The framework uses a moving grid to follow the trajectory of the UAV, reducing computational resources. Together with the ability to allow internal grid deformation, sliding mesh, and algorithm addition, it can accurately mimic the entire landing process. Different wing deformation speeds, additional elevator rotation and emulated propeller lift were added to the 6DOF simulations to investigate their effects on the landing maneuver. The results showed that the wing deformation retraction speed has a considerable effect on the trajectory and velocity of the UAV. The wing deformation reduced the forward velocity of the UAV by 32%, from 13.89 to 9 m/s. With the elevator control, the velocity was reduced to 5 m/s. Lastly, and an activation time of 1 s for the emulated propeller lift can further decrease the velocity to around 4.2 m/s. A better algorithm for the emulated propeller lift may be able to give a superior performance. This framework allows us to understand the underlying perch landing maneuver aerodynamics. It can also be used on problems like fast-turning agile and flapping wing flight.

A novel reconfigurable UAV design and control based on the parallel linkage

To enhance the maneuverability of unmanned aerial vehicles (UAVs) in confined spaces, this paper proposes a novel reconfigurable multi-rotor UAV. The UAV uses servos to drive its arms and thus change its configuration, enabling it to alter its morphology like a bird, reduces the largest dimension of the vehicle by approximately 41%↑. The body is connected to the motor base through a linkage mechanism, allowing synchronized movement between the arm and the motor. This synchronization helps the UAV to reduce the loss of thrust suffered after morphology transformation. This paper analyzes and deduces the changes in physical characteristic parameters during the process of UAV reconfiguration. It combines the active disturbance rejection control with the backstepping-based super-twisted sliding mode control to design a cascade control strategy that suppresses the disturbance caused by the UAV reconfiguration on flight control. The simulation and physical flight experiments demonstrate that the UAV is capable of reconfiguring into multiple morphologies during flight while maintaining flight stability throughout the reconfiguration process.

Enhanced Vision-Based Taillight Signal Recognition for Analyzing Forward Vehicle Behavior.

This study develops a vision-based technique for enhancing taillight recognition in autonomous vehicles, aimed at improving real-time decision making by analyzing the driving behaviors of vehicles ahead. The approach utilizes a convolutional 3D neural network (C3D) with feature simplification to classify taillight images into eight distinct states, adapting to various environmental conditions. The problem addressed is the variability in environmental conditions that affect the performance of vision-based systems. Our objective is to improve the accuracy and generalizability of taillight signal recognition under different conditions. The methodology involves using a C3D model to analyze video sequences, capturing both spatial and temporal features. Experimental results demonstrate a significant improvement in the model's accuracy (85.19%) and generalizability, enabling precise interpretation of preceding vehicle maneuvers. The proposed technique effectively enhances autonomous vehicle navigation and safety by ensuring reliable taillight state recognition, with potential for further improvements under nighttime and adverse weather conditions. Additionally, the system reduces latency in signal processing, ensuring faster and more reliable decision making directly on the edge devices installed within the vehicles.

Adaptive Frame Structure Design for Sensing-Assisted Downlink Communication in the Vehicle-to-Infrastructure Scenario.

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle's movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle's starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle's position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance.

Autonomous ground vehicle gravity anomaly measurement and dynamic error compensation

Abstract To address the issue that dynamic gravity anomaly measurement is overly dependent on GNSS and can’t be measured autonomously at this stage, this paper proposes an autonomous ground vehicle dynamic gravity anomaly measurement method based on a strapdown inertial navigation system (SINS), odometer (OD), barometer and platform gravimeter. The SINS/OD /barometer integrated navigation solution delivers high-precision navigation parameters, completes the calculation of correction terms, and performs the autonomous dynamic gravity anomaly measurement combined with the primary measurement results of the platform gravimeter. Numerical calculations provide the requirements for the application of the proposed method, and the cut-off frequency for extracting gravity anomalies is 0.02 Hz, as determined by power spectral density analysis. In order to further improve the measurement accuracy and account for dynamic errors caused by vehicle maneuvering, a long-short-term memory (LSTM) model of recurrent neural network (RNN) is introduced. A series of experiments under multiple circumstances with repeated lines were conducted in Tianjin, China, and the static measurements along the line were taken using CG-5 to provide true values of gravity anomalies. The results demonstrate that the autonomous measurement scheme can achieve accuracy comparable to GNSS-assisted, and that dynamic error compensation algorithm based on LSTM improves the dynamic gravity measurements accuracy significantly without sacrificing the spatial resolution of gravity anomalies.