Steering Maneuvers Research Articles

Lateral Vehicle Trajectory Planning Using a Model Predictive Control Scheme for an Automated Perpendicular Parking System

This article proposes a lateral vehicle trajectory planning and control algorithm using a model predictive control (MPC) scheme for an automated perpendicular parking system. Previous work showed that approximated clothoid-based local path planning using a virtual towing distance provides low computational time and the stability of lateral vehicle motion in a parking system. However, this approach cannot function in certain parking situations and may cause undesirable steering maneuvers due to state-dependent planning. We present a new approximated clothoid path based on lateral vehicle kinematics to cope with these problems. Using the proposed kinematic model, we formulate an MPC problem for path planning. Then, the proposed lateral vehicle trajectory planning becomes an MPC problem under constraints. Besides, we show that lateral vehicle motion for vertical parking can be implemented using simple kinematic lateral motion control. Experimental results show that the vehicle with the proposed method moves smoothly and completes the given parking mission under constraints, even under tight parking space conditions.

Haptic Assistive Control With Learning-Based Driver Intent Recognition for Semi-Autonomous Vehicles

Semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. Further, the learning-based model has a 30.9% higher driver intent recognition accuracy than the finite state machine-based method. The holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer operation.

Coordinated Control of Active Suspension and DYC for Four-Wheel Independent Drive Electric Vehicles Based on Stability

Active suspension control and direct yaw-moment control (DYC) are widely used in the vehicle control field. To solve the coupling between those two controllers, a coordinated control of active suspension and DYC is proposed to further improve the vehicle roll and yaw stability. To enhance the adaptive ability of the active suspension, a proportional integral control optimized by the genetic fuzzy algorithm is introduced. DYC is proposed based on the sliding mode control. To restrain the chattering, the parameters of the sliding mode control is optimized by a genetic algorithm. Finally, a coordinated controller is presented based on the adaptive distribution of the anti-roll torque in the front and rear suspension. The simulation results show that the proposed active suspension and DYC can greatly improve the roll and yaw stability, respectively. The expected vehicle status can be well tracked. In addition, the coordinated control is compared by simply using two independent controllers under a different tire–road friction coefficient and different steering maneuver. The results show that the coordinated control has an even better performance under each working condition.

Advanced Steering Maneuvers for Special Situations in Mitral Valve Transcatheter Edge-to-edge Repair

The use of transcatheter percutaneous edge-to-edge mitral valve repair with the MitraClip (Abbott) for the treatment of mitral regurgitation has rapidly expanded since it received Food and Drug Administration approval. Currently, patients with more complex mitral valve anatomies are undergoing this procedure with progressively improving clinical outcomes. This review focuses on the advanced steering maneuvers needed for the technical success of the MitraClip procedure and discusses several scenarios where such advanced steering maneuvers may be needed. These include an anterior transseptal puncture, low or high transseptal puncture with unfavorable transseptal height to mitral valve, large gap height, patients with mitral regurgitation due to dehiscence of a prior mitral valve annuloplasty ring, placing multiple/adjacent MitraClips, or redo edge-to-edge mitral valve repair. Knowledge and mastery of such advanced steering maneuvering techniques can allow operators to complete the procedure successfully with low rate of complications even in difficult scenarios.

Design and Optimization of an Untethered High-Performance Robotic Tuna

For bioinspired underwater robots, it is a great challenge to achieve both high swimming speed and steering maneuverability. To this end, this article presents an untethered high-performance robotic tuna through mechanism optimization and steering strategies design. First, combining the advantages of single-joint and multijoint robotic fish, a novel mechanism of redundant joints is designed to enrich the swimming patterns of the robotic tuna. Next, a three-dimensional dynamic model is established, and the model parameters are accurately identified by experimental data. With the aid of the model and experiments, the speed performance is optimized under the key parameters of tail fin, such as size and flexibility. Further, from the perspective of imitating tuna and the demand of actual operations, two steering strategies are proposed to improve the steering performance. Finally, extensive simulation and experiments verify the effectiveness of the proposed methods. The obtained results reveal that the untethered robotic tuna can achieve both high swimming speed with 2.26 m/s (equivalent to 3.13 body lengths per second) and outstanding steering maneuverability with 0.48 body lengths in turning radius, providing valuable insight into performing special missions in cluttered ocean environments.

Integrated Adaptive Steering Stability Control for Ground Vehicle with Actuator Saturations

During a steering manoeuvre in a ground vehicle, both yaw motion and roll motion can occur simultaneously, and their dynamics can be coupled, as the roll motion is generalized directly from the tires’ lateral force under steering. Hence, it is of significance to analyze them as an integrated plant in the vehicle steering stability control problem. Furthermore, the actuator saturation of yaw control cannot be neglected, as vehicles often steer at a high velocity or on low-friction roads. In this paper, an integrated steering dynamics model is established considering the coupling between the roll motion and lateral motion, then a novel nonlinear adaptive controller is proposed to stabilize the steering motion considering the actuator saturation of yaw motion control. Simulation results indicate that the designed integrated controller is effective in improving the performance of both the yaw rate tracking error and ride comfort taking into account vehicle parameter uncertainties and actuator saturation; the steering stability of ground vehicles can consequently be guaranteed.

Energy Management Strategy in 12-Volt Electrical System Based on Deep Reinforcement Learning

The increasing electrification in motor vehicles in recent decades can be attributed to higher comfort and safety demands. Strong steering and braking maneuvers reduce the vehicle’s electrical system voltage, which causes the vehicle electrical system voltage to drop below a critical voltage level. A sophisticated electrical energy management system (EEMS) is needed to coordinate the power flows within a 12-volt electrical system. To prevent the voltage supply from being insufficient for safety-critical consumers in such a case, the power consumption of several comfort consumers can be reduced or switched off completely. Rule-based (RB) energy management strategies are often used for this purpose, as they are easy to implement. However, this approach is subject to the limitation that it is vehicle-model-specific. For this reason, deep reinforcement learning (DRL) is used in the present work, which can intervene in a 12-volt electrical system, regardless of the type of vehicle, to ensure safety functions. A simulation-based study with a comprehensive model of a vehicle electric power system is conducted to show that the DRL-based strategy satisfies the main requirements of an actual vehicle. This method is tested in a simulation environment during driving scenarios that are critical for the system’s voltage stability. Finally, this is compared with the rule-based energy management system using actual vehicle measurements. Concluding measurements reveal that this method is able to increase the voltage at the most critical position of the 12-volt electrical system by approximately 0.6 V.

Research on Lane Change Motion Planning Steering Input Based on Optimal Control Theory

Aiming at traditional control methods are not suitable for solving the multiconstraint problem of the vehicle steering and lane-change maneuvering process, a hybrid optimization scheme based on dynamic adaptive salp swarm algorithm and Gaussian pseudospectral method (DASSA-GPM) is proposed for its strong global search capability. Based on a steering inverse dynamics model, the lane change motion planning steering problem is converted into an optimal control problem which is then converted into a nonlinear programming problem by applying the adaptive salp group algorithm which is solved through the sequential quadratic programming (SQP) method finally. The simulation results verify the effectiveness and feasibility of the proposed dynamic adaptive salp swarm algorithm and hybrid optimization scheme. In addition, compared with GPM which the absolute error of steering wheel angle is 8.2 × 10−4 degree, the proposed method has higher computational accuracy, which absolute error of steering wheel angle is 5.5 × 10−4 degree. At the same time, under the same calculation accuracy (10−3), compared with GPM which CPU time is 4.81 s, the proposed method has higher calculation efficiency which CPU time is 3.86 s when solving non-smooth problems. The proposed method can provide a reference value into the active safety of manned and unmanned vehicles.

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics

We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. In our approach, an automatic guided vehicle is equipped with two LiDAR sensors and one frontal RGB camera and learns to perform a targeted navigation task. The challenges reside in the sparseness of positive samples for learning, multi-modal sensor perception with partial observability, the demand for accurate steering maneuvers together with long training cycles. To address these points, we propose NavACL-Q as an automatic curriculum learning method in combination with a distributed version of the soft actor-critic algorithm. The performance of the learning algorithm is evaluated exhaustively in a different warehouse environment to validate both robustness and generalizability of the learned policy. Results in NVIDIA Isaac Sim demonstrates that our trained agent significantly outperforms the map-based navigation pipeline provided by NVIDIA Isaac Sim with an increased agent-goal distance of 3 m and a wider initial relative agent-goal rotation of approximately 45∘. The ablation studies also suggest that NavACL-Q greatly facilitates the whole learning process with a performance gain of roughly 40% compared to training with random starts and a pre-trained feature extractor manifestly boosts the performance by approximately 60%.

Deep reinforcement learning based active safety control for distributed drive electric vehicles

Distributed drive electric vehicles are regarded as the promising transportation due to the advanced power flow architecture. Optimizing the yaw motion to enhance vehicle safety is a challenging job. Besides, the nonlinear features in vehicles affect the control accuracy of the yaw motion controllers. To this end, a deep reinforcement learning (DRL) based direct yaw moment control (DYC) strategy is put forward here. Vehicle dynamics can be approximated with the DRL algorithm, which reduces the complex nonlinear solving process. Concretely, the DYC problem is formulated as Markov Decision Process in which the observed signals and external yaw moment are incorporated as the state and action sets. Thereupon, actor-critic network is exhibited to approximate action-value function and policy function for better control performance. Furthermore, to guarantee the continuous solution of external yaw moment, the deep deterministic policy gradient algorithm is employed, in which target and online network parameters are simultaneously trained to maintain learning process stability. The proposed DRL based DYC strategy is verified using the Carsim/Simulink platform under the typical lane change manoeuvres. Numerical test results demonstrate that the proposed DYC strategy outperforms the linear approaches on taking full advantage of understeer features and enhancing the lateral stability, especially under the critical steering manoeuvres.

Study on the Maximum Safe Instantaneous Input of the Steering Wheel against Rollover for Trucks on Horizontal Curves.

Truck rollover crashes on horizontal curves have been recognized as one of the most serious types of crashes. Driver’s instantaneous emergency steering maneuvers (DIESM) play an important role in truck rollover crashes, but have not received much attention. In the present study, the radius of curvature of the actual vehicle travel path (AVTP) under DIESM was calculated based on the transient bicycle model. Rollover margins were used to evaluate the truck-rollover potential under DIESM. To calculate rollover margins, the lateral acceleration under DIESM was calculated based on the radius of the curvature of the AVTP. A rollover threshold formula was introduced to calculate vehicle’s rollover thresholds by distinguishing two turning conditions. According to rollover margins, the maximum safe instantaneous input of the steering wheel against rollover for trucks was obtained. Moreover, theoretical results were verified by computer simulation. Results showed: (1) The maximum safe instantaneous inputs of the steering wheel were 259°, 212°, 182°, 162°and 147°, respectively, at speeds of 60 km/h, 70 km/h, 80 km, 90 km and 100 km when the superelevation rate was 0, and (2) superelevation significantly affected truck-rollover potential; the worst turning condition was turning from the inside to the outside of the curve. Due to the consideration of the wheelbase, the centroid position, the tire’s cornering stiffness and the suspension roll gain, the prediction results were more accurate.

Hydraulic control valve integrated novel semi active roll resistant interconnected suspension with vertical and roll coordinated control scheme

Though the interconnected active suspension enhances the performances of roll dynamics along with the primary functions of passive suspension, it requires an external pressurized fluid supply. Also, higher energy is required to attain the variable damping force in an active interconnected system. Therefore, this research attempts to control the vehicle roll without an external fluid supply by novel semi-active based roll-resistant interconnected suspension system (SARR-HIS). The variable damping is achieved by utilizing the pressure developed in the chambers and hydraulic control valve (HCV), while the suspension compresses. The amount of the flow passes through the HCV is controlled by a model predictive controller. In addition, GA based optimization were performed to identify the optimal passive roll-resistant hydraulic interconnected suspension (PRR-HIS) parameters. For the controller design, vehicle plant model is estimated by the simple identification method in Matlab environment. On the AMESim platform, vehicle with SARR-HIS system integrated model was built and controlled in a co-simulation environment. To assess the effectiveness of the proposed suspension and its control strategy, the anti-roll bar (ARB) integrated passive standalone suspension, optimized PRR-HIS, and proposed SARR-HIS systems are tested under C class road roughness without steer and double lane change maneuver.

Vehicle stability controller HiL validation on static simulator

In the automotive world, Hardware in the Loop (HiL) methodologies are used to speed up the design and calibration process of the various mechatronics components installed on the vehicle. Installing production parts on advanced driving simulator enables the performance assessment in realistic scenarios, including a test driver earlier in the design process. The scope of this work is to evaluate the effect of ABS and ESC system, replicating their influence on the steering wheel. A brake by wire production system has been installed on a static simulator, equipped with a EPSiL steering bench, capable of accurately replicate tie-rod forces on a production steering unit. Full brake, sine steer maneuvers have been carried out both on real vehicle and on virtual environment. The system showed its capabilities of replicating the same functionalities of the real vehicle, extending the static simulator potentialities to support the activities on calibration and test of vehicle systems.

Correlation Analysis of Energy Consumption of Agricultural Rotorcraft

With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles (UAVs) have been widely used in the field of agricultural plant protection. Compared with fuel-driven UAVs, electrically driven rotorcrafts have many advantages such as lower cost, simpler operation, good maneuverability and cleaner power, which them popular in the plant protection. However, electrical rotorcrafts still face battery problems in actual operation, which limits its working time and application. Aiming at this issue, this paper studied the influence of rotorcraft flight parameters on energy consumption through series of carefully designed flight experiments. First of all, the linear motion experiments have been designed that the rotorcraft was made to perform speed tests and acceleration test with the speed varied from 2∼9 m/s. Secondly, the turning maneuver experiments are carried out under the different circular routes, a rotorcraft was made to conduct successive steering maneuvers at a certain speed of 2 m/s. With the collected tests data, the relation of the energy consumption and the flight dynamic parameter are analyzed through correlation analysis, and the test results of different pairs of experiments have been compared. The research results of this paper would encourage the agricultural rotorcraft to make less maneuvers during operation, which can also provide practical experience and data support for subsequent optimization of flight parameters and reduction of energy consumption.

Conflict and Sensitivity Analysis of Articulated Vehicle Lateral Stability Based on Single-Track Model

Lateral stability is quite essential for the vehicle. For the vehicle with an articulated steering system, the vehicle load and steering system performance is quite different from the passenger car with the Ackman steering system. To investigate the influence of the tire characteristics and vehicle parameters on lateral stability, a single-track dynamic model is established based on the vehicle dynamic theory. The accuracy of the built model is validated by the field test result. The investigated parameters include the tire cornering stiffness, vehicle load, wheelbase, and speed. Based on the snaking steering maneuver, the lateral stability criteria including the yaw rate, vehicle sideslip angle, tire sideslip angle, and lateral force are calculated and compared. The sensitivity analysis of the tire and vehicle parameters on the lateral stability indicators is initiated. The results demonstrated that the parameters that affect the lateral vehicle stability the most are the load on the rear part and the tire cornering stiffness. The findings also lay a foundation for the optimization of the vehicle’s lateral stability.

3D-Printed Miniature Robots with Piezoelectric Actuation for Locomotion and Steering Maneuverability Applications

The miniaturization of robots with locomotion abilities is a challenge of significant technological impact in many applications where large-scale robots have physical or cost restrictions. Access to hostile environments, improving microfabrication processes, or advanced instrumentation are examples of their potential use. Here, we propose a miniature 20 mm long sub-gram robot with piezoelectric actuation whose direction of motion can be controlled. A differential drive approach was implemented in an H-shaped 3D-printed motor platform featuring two plate resonators linked at their center, with built-in legs. The locomotion was driven by the generation of standing waves on each plate by means of piezoelectric patches excited with burst signals. The control of the motion trajectory of the robot, either translation or rotation, was attained by adjusting the parameters of the actuation signals such as the applied voltage, the number of applied cycles, or the driving frequency. The robot demonstrated locomotion in bidirectional straight paths as long as 65 mm at 2 mm/s speed with a voltage amplitude of only 10 V, and forward and backward precise steps as low as 1 µm. The spinning of the robot could be controlled with turns as low as 0.013 deg. and angular speeds as high as 3 deg./s under the same conditions. The proposed device was able to describe complex trajectories of more than 160 mm, while carrying 70 times its own weight.

Parameter influence analysis in an optimized fuzzy stability control for a four-wheel independent-drive electric vehicle

This paper presents an optimized fuzzy logic control (FLC) applied to four-wheel independent-drive electric vehicles (FWID-EVs). The controller is developed to act as an Electronic Differential (ED), which changes the in-wheel motors torques to correct the vehicle trajectory during some specific maneuvers. The simulation determines the rotation, the lateral and longitudinal displacement of the vehicle, in which the nonlinear Magic Formula is applied to define the tires slip, associated with the load transfer during curves or drive/break situations and with the steering input. According to the literature, several parameters are used to provide stability controls of similar vehicles, for example, the sideslip angle and yaw rate. Therefore, a parameter influence analysis is carried out to find out the most relevant parameters to be monitored in the investigated FWID-EV. Once the relevant input parameters are defined, an optimum ED FLC is developed by means of a genetic algorithm multi-objective optimization with the objective of minimizing the trajectory error of the FWID-EV under a combination of the Sine with Dwell and constant steering standard maneuvers. The results show the best control configuration is a combination of steer and sideslip angles, with a reduced number of rules and less processing time (0.71 s). Besides, the proposed control presents a reduction in the trajectory error, improvement up to 95.1%, emphasizing that the parameter influence analysis allows finding an optimum, more efficient, and time-saving control solution. Therefore, a simple adjustment in the variables inputs can decrease the control process, without overshadowing the dynamic behavior.

Modeling, bench test and ride analysis of a novel energy-harvesting hydraulically interconnected suspension system

A novel energy-harvesting hydraulically interconnected suspension (EH-HIS) is proposed, modeled, built and tested in this paper to enhance riding comfort and road handling performances for vehicles while converting the vibration energy traditionally wasted into usable electricity. A sub-module prototype has been developed to validate the analytical model and tested to study the damping characteristics and energy harvesting capability of the EH-HIS. The studies are performed to compare the dynamic responses and energy harvesting performance of the full-vehicle equipped with the traditional suspension, hydraulically interconnected suspension (HIS), hydro-electromagnetic energy-harvesting shock absorber (HESA) and the proposed EH-HIS, respectively. Bench test results show that the damping coefficient of the EH-HIS ranges from 3608 to 9913 Ns/m. The average harvested power of the sub-module reaches 82 W at the excitation of 2 Hz frequency and 20 mm amplitude, while the full-size SUV equipped with the EH-HIS can obtain 215 W and 525 W average power on D-class road with a driving velocity of 60 km/h and 80 km/h, respectively. Moreover, the vehicle equipped with EH-HIS has better riding comfort on rough terrains and satisfying handling stability compared with other suspension systems. The analysis also indicates that EH-HIS exhibit 60% and 11% improvements in the anti-rolling and anti-pitching performances over the traditional suspension during emergency steering and braking maneuvers.

A methodology for assessing driver perception-response time during unanticipated cross-centerline events

Objective The purpose of this study was to present a methodology that utilizes naturalistic driving data to measure the driver response to an unanticipated driving scenario, a cross-centerline event. Methods Forward-facing video from naturalistic driving was used to determine when the cross-centerline event occurred. Then, the recorded acceleration and yaw rate data were used to identify the start of braking and steering evasive actions, respectively. A deceleration threshold of −0.1 g was defined as the braking onset, and a yaw rate of 2 deg/s was defined as the steering onset. Perception-response times (PRTs) were derived using these inputs. Results 17 cross-centerline events were identified from the naturalistic driving database. The drivers in all analyzed events applied the brakes, and 11 of the 17 drivers performed a steering maneuver. However, the average steering PRT (0.39 s) was faster than the average braking PRT (0.84 s). Conclusions Based naturalistic data from cross-centerline encroachment scenarios, the average driver steering PRT was faster than the average driver braking PRT. Both the driver’s median braking and steering PRT was faster in these real-world scenarios than in similar test track or simulator studies. Future analyses should investigate which action is attempted first and the effect of time to contact on driver response.

Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite

Path planning and collision avoidance during autonomous navigation in unknown environments is a crucial issue for unmanned surface vehicles (USVs). This paper improves the traditional D* Lite algorithm and achieves multi-goal path planning and collision avoidance for USVs in unknown and complex environments. By expanding the adjacent search range and setting a safe distance for USVs, we solve the issue of limited steering maneuverability in USVs with fewer DOF during autonomous navigation. We propose an approach to optimize the planned path during navigation by comparing the estimated distance with the actual distance between the current waypoint and the goal waypoint. A minimum binary heap is used to optimize the priority queue of the D* Lite and significantly reduce the path search time. Simulation results show that the improved D * Lite can significantly reduce the path planning time, optimize the planned path and solve the issue of limited steering maneuverability in USVs. We apply the algorithm to a real USV for further tests. Experimental results show that the USV can plan an optimized path while avoiding both static and dynamic obstacles in complex environments with a safe distance during autonomous navigation.