Browse Topic: Steering systems

Items (2,080)
To address the issues of unreasonable collision avoidance path planning algorithms and inadequate safety in high-speed scenarios, a trajectory prediction-based collision avoidance path planning algorithm has been proposed. First, a trajectory prediction model is constructed using the long–short-term memory (LSTM) network, and the trajectory prediction model is trained and tested with the HighD dataset. Second, the future trajectory of the obstacle car is predicted, the future trajectory information of the two cars is combined to generate the lane-changing decision, and the three-times B-spline curves are used to generate the collision avoidance path clusters. The optimal collision avoidance paths are generated based on the multi-objective optimization function. Finally, build a MATLAB/CarSim simulation platform to verify the reasonableness and safety of the planned paths by taking the three scenarios of the continuous overtaking, preceding car pulling out, and the neighboring car
Liu, Xiao LongZhang, LeiLi, Peng KunXie, RuWang, QingLi, Ran Ran
Accurate and responsive trajectory tracking is a critical challenge in intelligent vehicle control system. To improve the adaptability and real-time performance of intelligent vehicle trajectory tracking controllers, we propose a genetic algorithm adaptive preview (GAAP) scheme that offline optimizes the preview distance based on vehicle speed and reference path curvature. The goal is to obtain the optimal preview distance that balances tracking accuracy, stability, and real-time performance. By establishing a relationship between optimal preview distance, speed, and curvature, we enhance real-time performance through online table checking during trajectory tracking. Our trajectory tracking error model takes into account not only position errors but also heading errors. A feedback–feedforward trajectory tracking controller is then designed to achieve rapid responses without compromising robustness. Simulation tests conducted under straight circular arc condition and double lane change
Cheng, KehanZhang, HuanhuanHu, ShengliNing, Qianjia
This research, path planning optimization of the deep Q-network (DQN) algorithm is enhanced through integration with the enhanced deep Q-network (EDQN) for mobile robot (MR) navigation in specific scenarios. This approach involves multiple objectives, such as minimizing path distance, energy consumption, and obstacle avoidance. The proposed algorithm has been adapted to operate MRs in both 10 × 10 and 15 × 15 grid-mapped environments, accommodating both static and dynamic settings. The main objective of the algorithm is to determine the most efficient, optimized path to the target destination. A learning-based MR was utilized to experimentally validate the EDQN methodology, confirming its effectiveness. For robot trajectory tasks, this research demonstrates that the EDQN approach enables collision avoidance, optimizes path efficiency, and achieves practical applicability. Training episodes were implemented over 3000 iterations. In comparison to traditional algorithms such as A*, GA
Arumugam, VengatesanAlagumalai, VasudevanRajendran, Sundarakannan
Autonomous driving technology has indeed become a focal point of research globally, with significant efforts directed towards enhancing its key components: environment perception, vehicle localization, path planning, and motion control. These components work together to enable autonomous vehicles to navigate complex environments safely and efficiently. Among these components, environment perception stands out as critical, as it involves the robust, real-time detection of targets on the road. This process relies heavily on the integration of various sensors, making data fusion an indispensable tool in the early stages of automation. Sensor fusion between the camera and RADAR (Radio Detection and Ranging) has advantages because they are complementary sensors, where fusion combines the high lateral resolution from the vision system with the robustness in the face of adverse weather conditions and light invulnerability of RADAR, as well as having a lower production cost compared to the
Cury, Hachid HabibTeixeira, Evandro Leonardo SilvaSilva, Rafael Rodrigues
The SAE Formula, a national stage of the international competition, consists of a student project at universities in Brazil that seeks to encourage engineering students to apply the theoretical knowledge obtained in the classroom to practice, dealing with real problems and difficulties in order to prepare them for the job market. The SAE Formula prototype is developed with the intention of competing in the SAE national competition, where teams from various universities in Brazil meet to compete and demonstrate the projects developed during the year. Focusing on the vehicle dynamics subsystem, which can be divided into the braking, suspension, and steering systems of a prototype, the steering system includes main mechanical components such as the front axle sleeves, wheel hub, steering arm, steering column, rack, wheel, and tire. All these components work together with the suspension systems, including suspension arms, “bell crank,” and spring/shock absorber assembly. These components
Rigo, Cristiano Shuji ShimadaNeto, Antonio Dos Reis De FariaGrandinetti, Francisco JoseCastro, Thais SantosDias, Erica XimenesMartins, Marcelo Sampaio
The exponential growth of the agribusiness market in Brazil combined with the high receptivity among farmers of new technological solutions has driven the study and implementation of high technology in the field. This work aimed to apply servo-assisted driving technology to enable autonomous mobility in an off-road sugarcane truck responsible for harvesting sugarcane. The technology consists of a conventional hydraulic steering with a motor, ECU and torque and angle sensors responsible for reading input data converted from GPS signals and previously recorded tracking lines. The motor responsible for replacing 100% of the physical force generated by the driver acts in accordance with the required torque demand, and the sensors combined with the ECU correct the course to meet the follow-up line through external communication ports. The accuracy of the system depends exclusively on the accuracy of the GPS signal, in this case reaching 2,5 cm, which is considered extremely high accuracy
Oliveira Santos Neto, AntídioLara, VanderleiSilva, EvertonDestro, DanielMoura, MárcioBorges, FelipeHaegele, Timo
Single lane changing is one of the typical scenarios in vehicle driving. Planning an appropriate lane change trajectory is crucial in autonomous and semi-autonomous vehicle research. Existing polynomial trajectory planning mostly uses cubic or quintic polynomials, neglecting the lateral jerk constraints during lane changes. This study uses seventh-degree polynomials for lane change trajectory planning by considering the vehicle lateral jerk constraints. Simulation results show that the utilization of the seventh-degree method results in a 41% reduction in jerk compared to the fifth-degree polynomial. Furthermore, this study also proposes lane change trajectory schemes that can cater to different driving styles (e.g., safety, efficiency, comfort, and balanced performance). Depending on the driving style, the planned lane change trajectory ensures that the vehicle achieves optimal performance in one or more aspects during the lane change process. For example, with the trajectory that
Lai, FeiHuang, Chaoqun
SBW(Steer-by-wire) is a steering system that transmits the driver’s request and gives feedback to the driver through electrical signals. This system eliminates the mechanical connection of the traditional steering system, and can realize the decoupling of the steering wheel and the road wheel. In addition, this system has a perfect torque feedback system, which can accurately and delicately feedback the road surface information to the driver. However, vehicle driving deviation is one of the most common failure modes affecting vehicle performance in the automotive aftermarket, this failure mode can exacerbates tire wear, reducing their life cycle, at the same time, the driver must apply a counter torque to the steering wheel for a long time to maintain straight-line travel during driving. This increases the driver’s operational burden and poses safety hazards to the vehicle’s operation. Based on the steer-by-wire system and vehicle driving deviation characteristics, this paper proposes
Xiangfei, XuQu, Yuan
Path planning in parking scenarios for vehicles with Ackermann steering characteristics is a well studied problem in the literature. However, the recent emergence of four-wheel steering (4WS) chassis has brought new opportunities to the field of motion planning. Compared with front-wheel steering (2WS), 4WS vehicles offer higher flexibility and new maneuver modes such as CrabWalk. To utilize such new potential to further improve parking efficiency, this paper proposes a four-wheel steering oriented planning algorithm for parking scenarios. First, Hybrid A*-4WS is proposed to search for a coarse trajectory from the starting pose to the parking slot, with improved node expansion mechanism to incorporate four-wheel steering characteristics. Then a nonlinear programming (NLP) problem is formulated with four-wheel steering kinematic model to fully utilize the maneuver capability of 4WS vehicles, with OBCA used for collision avoidance constraints. Finally, the two algorithms are sequentially
Song, YufeiLiu, YuanzhiXiong, LuTang, Chen
This paper proposes a path-tracking and direct yaw moment integrated control strategy based on linear matrix inequality (LMI) and terminal sliding mode for autonomous distributed drive electric vehicles (A-DDEVs) equipped with a steer-by-wire (SBW) system. This strategy effectively attenuates the effects of external disturbances and parameter uncertainties on path tracking, thereby enhancing vehicle safety. The control-oriented vehicle model accounts for roll effects, with the system state matrix incorporating mismatched norm bounded uncertainties. Firstly, for overall vehicle motion control, an LMI-based integral sliding mode controller (ISMC) is designed to generate desired front wheel steering angle and additional yaw moment. This aims to converge path-tracking errors and ensure vehicle stability. A sufficient condition for the existence of a sliding surface ensuring asymptotic stability of the sliding mode dynamics is provided, along with a demonstration of the attainability of the
Li, DanyangZhao, YouqunLin, FenZhang, ChenxiYu, Song
The application trend of automated driving is gaining significant concern, making it increasingly crucial to validate automated driving within the stochastic simulated traffic flow environment from both time and cost perspectives. The stochastic traffic flow model attempts to encapsulate the variability inherent in traffic conditions through a stochastic process. This approach is particularly important as it accounts for the unpredictable nature of traffic, which is often not fully captured by traditional deterministic testing scenarios. However, while stochastic traffic flow models have made strides in simulating the behavior of traffic participants, there remains a significant oversight in the simulation of vehicles’ driving trajectories, leading to unrealistic portrayals of their behaviors. The trajectories of vehicles are a critical component in the overall behavior of traffic flow, and their accurate representation is essential for the simulation to reflect real-world driving
Gao, YiboCao, PengYang, Aixi
Learning-based motion planning methods such as reinforcement learning (RL) have shown great potential of improving the performance of autonomous driving. However, comprehensively ensuring safety and efficiency remain a challenge for motion planning technology. Most current RL methods output discrete behavioral action or continuous control action, which lack an intuitive representation of the future motion and then face the problems with unstable or reckless driving behavior. To address these issues, this work proposes an interaction-aware reinforcement learning approach based on hybrid parameterized action space for autonomous driving in lane change scenario. The proposed method can output high-level feasible trajectory and low-level actuator control command to control the vehicle’s motion together. Meanwhile, the reward functions for the local traffic environment are designed to evaluate the effect of the interaction between ego vehicle and surrounding vehicles. The contributions of
Li, ZhuorenJin, GuizheYu, RanLeng, BoXiong, Lu
There is evidence to suggest that males and females respond differently in motor vehicle collisions, making it important to study how both sexes respond to vehicle safety systems. The THOR 5th-percentile female (THOR-05F) anthropomorphic test device (ATD) was developed to represent a small female occupant better than the Hybrid III 5th-percentile female (HIII-05F) ATD. However, there are few studies in which they have been directly compared. Therefore, the objective of this study was to compare the responses of the two ATDs in matched frontal sled tests simulating a realistic driver seat environment. A 7th-generation Toyota Camry driver seat test buck was used with Camry parts (i.e., 3-point belt, modified seat, steering wheel, airbag, and column). The belt was equipped with a 4-kN load limiter and pretensioner. Rigid foam (65 psi) was used to represent the knee bolster. Thirteen tests were conducted using speeds of 30 and 56 kph. Chest bands were used to measure external chest
Boyle, David M.Albert, Devon L.Hardy, Warren N.Kemper, Andrew R.
Turning circle diameter (TCD) of vehicle is critical parameter which is used to determine the turning capability of vehicle. TCD is the smallest circular turn that a vehicle can make of given drive track. The TCD depends on vehicle wheel lock angles, wheelbase, and geometric architecture of vehicle. The Regulation certification requirement of steering system, states that the maximum TCD should be less than 24m & TCCD (Turning clearance circle diameter) 25m (M&N category Vehicle). IS 12222:2011 & UN R79 are regulation related to Steering system. This invention relates to measuring the TCD of vehicle. The conclusion of this technical paper proposes new innovative method to overcomes and address the below limitations. It provides accurate and precise results by adjusting room for error. It eliminates the approximation ambiguity. It reduces the manual intervention of human effort to carry out the entire measurement process. It improves the safety of measurement technician, since it
Yadav, SatyendraOjha, VijayChatterjee, AnupamSaikrishna, VNLKarthik, V
The structural integrity of the steering wheel is important for vehicle operations. It is subjected to various load conditions during the vehicle motion. It thus becomes important to understand various aspects of the same which include stiffness, natural frequency, and regulatory requirements i.e. body block test, head form impact test, etc. Simulation plays an important role in understanding the structural integrity and validation requirements of products at the design stage itself. This paper discusses the modeling and simulation of the steering wheel at both the armature level and the complete steering wheel level. As armature is critical from a structural strength and stiffness point of view, certain simulations like modal analysis are performed first at the armature level, and design iterations were done to achieve the natural frequency target. The list of simulations performed includes modal analysis, bending rigidity, static compression, bending stiffness, body block test and
Rathore, Gopal SinghKumar, AnkitChauhan, Adesh KumarDas, A.P.Sahu, Hemanta Kumar
Investigation of clunking noise in a steering system fitted into a test vehicle indicated radial lash in sliding bushings, which are press fitted into housings, as one of the possible causes for clunk. To study the behavior of sliding bush under the influence of assembly clamp loads, manufacturing tolerances and road loads, sliding bushings are modelled in more detail in the steering system finite element models. Further for correlating the bushing measurements from test vehicle to the finite element model, a radial lash output is derived which is not directly available in finite element software. Finally, the correlated model is used to assess the updated design and check for radial lash improvement
Badduri, JaideepPandey, Ashish
Hypersonic platforms provide a challenge for flight test campaigns due to the application's flight profiles and environments. The hypersonic environment is generally classified as any speed above Mach 5, although there are finer distinctions, such as “high hypersonic” (between Mach 10 to 25) and “reentry” (above Mach 25). Hypersonic speeds are accompanied, in general, by a small shock standoff distance. As the Mach number increases, the entropy layer of the air around the platform changes rapidly, and there are accompanying vortical flows. Also, a significant amount of aerodynamic heating causes the air around the platform to disassociate and ionize. From a flight test perspective, this matters because the plasma and the ionization interfere with the radio frequency (RF) channels. This interference reduces the telemetry links' reliability and backup techniques must be employed to guarantee the reception of acquired data. Additionally, the flight test instrumentation (FTI) package needs
ABSTRACT This paper presents two techniques for autonomous convoy operations, one based on the Ranger localization system and the other a path planning technique within the Robotic Technology Kernel called Vaquerito. The first solution, Ranger, is a high-precision localization system developed by Southwest Research Institute® (SwRI®) that uses an inexpensive downward-facing camera and a simple lighting and electronics package. It is easily integrated onto vehicle platforms of almost any size, making it ideal for heterogeneous convoys. The second solution, Vaquerito, is a human-centered path planning technique that takes a hand-drawn map of a route and matches it to the perceived environment in real time to follow a route known to the operator, but not to the vehicle. Citation: N. Alton, M. Bries, J. Hernandez, “Autonomous Convoy Operations in the Robotic Technology Kernel (RTK)”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI
Alton, NicholasBries, MatthewHernandez, Joseph
Internet of vehicles (IoV) system as a typical application scenario of smart city, trajectory planning is one of the key technologies of the system. However, there are some unstructured spaces such as road shoulders and slopes pose challenges for trajectory planning of connected-automated vehicle (CAV). Therefore, this paper addresses the problem of CAV trajectory planning affected by unstructured space. Firstly, based on cyber-physical system (CPS), the cyber-physical trajectory planning system (CPTPS) framework was built. A high-precision digital twin CAV is established based on the physical properties and geometric constraints of CAV, and the digital model is mapped to cyber space of the CPTPS. In order to further reduce the energy consumption of the CAV during driving and the time spent from the start to the end, a model was established. Further, based on the sand cat swarm hybrid particle swarm optimization algorithm (SCSHPSO), global path planning for connected-automated vehicles
Ma, ShiziMa, ZhitaoShi, YingYang, ZhongkaiLai, DaoyinQi, Zhiguo
ABSTRACT In the field of ground robotics, the problems of global path planning and local obstacle avoidance are often treated separately but both are assessed in terms of a cost related to navigating through a given environment. Traversal cost is typically defined in terms of the required fuel [1], required travel time [2], and imparted mechanical wear [3] to guide route selection. Prior work [4] has shown that obstacle field complexity and navigation cost can be abstracted into quantitative dimensionless parameters. But determining the cost parameters and their relationship to field complexity requires running repeated path planning simulations [4]. This work presents a method for estimating navigation cost solely from geometric obstacle field complexity measures, namely the statistical properties of an obstacle’s shape and the density of obstacles within an environment, eliminating the requirement to run a path planner in a simulation environment. Citation: S. J. Harnett, S. Brennan
Harnett, Stephen J.Brennan, SeanReichard, KarlPentzer, JesseTau, SethGorsich, David
ABSTRACT Path planning is critical for mission implementation in various robot platforms and autonomous combat vehicles. With the efforts of electrification, battery energy storage as power sources is an ideal solution for robots and autonomous combat vehicles to improve capability and survivability. However, the battery’s limited energy and limited instantaneous power capability could become limiting factors for a mission. The energy and power constraints are also affected by the environment, battery state of health (SOH), and state of charge (SOC) significantly; in the worst case, a well-tested mission profile could fail in the real world if all aspects of the battery are not considered. This paper presents a framework to model the battery’s capability to support a whole mission and specific tasks under various environments. This real-time battery model can be built into an intelligent battery management system to support system-level mission planning, real-time task selection
Nan, XiDong-O’Brien, JingYan, LiangLi, PengHundich, AlexSkalny, David
ABSTRACT Self-driving or autonomous vehicles consist of software and hardware subsystems that perform tasks like sensing, perception, path-planning, vehicle control, and actuation. An error in one of these subsystems may manifest itself in any subsystem to which it is connected. Errors in sensor data propagate through the entire software pipeline from perception to path planning to vehicle control. However, while a small number of previous studies have focused on the propagation of errors in pose estimation or image processing, there has been little prior work on systematic evaluation of the propagation of errors through the entire autonomous architecture. In this work, we present a simulation study of error propagation through an autonomous system and work toward developing appropriate metrics for quantifying the error at both the subsystem and system levels. Finally, we demonstrate how the framework for analyzing error propagation can be applied to analysis of an autonomous systems
Carruth, Daniel W.Goodin, ChristopherDabbiru, LalithaScherer, NicklausJayakumar, Paramsothy
ABSTRACT In order to introduce longer heavy vehicles with multiple articulation joints between vehicle units into the UK and other European countries, rear steering of the trailing vehicle units is required to allow for sufficient manoeuvrability. An extensive program of research has been undertaken into trailer steering technologies in recent years. Such systems can enable significantly longer heavy vehicles to negotiate narrow roads. It is thought that this same technology could be used in military supply operations. Possible benefits of using multiple-trailer ‘long combination’ vehicles in military supply include: (i) Fewer vehicles are needed to perform the same supply tasks. This means fewer drivers and consequently less exposure to threats, as well as improved productivity of each driver and vehicle. (ii) Longer vehicles can have 20% to 30% lower fuel consumption for a given freight task than conventional vehicles, depending on the configuration. Application of controlled
Cebon, DavidRoebuck, RichardOdhams, Andrew
ABSTRACT Autonomous ground vehicles have the potential to reduce the risk to Soldiers in unfamiliar, unstructured environments. Unmanned operations in unstructured environments require the ability to guide the vehicles from their starting position to a target position. This paper proposes a framework to plan paths across such unstructured environments using a priori information about the environment as cost criteria into a multi-criteria, multi-agent path planner. The proposed multi-criteria, multi-agent path planner uses a penalty-based A* algorithm to plan multiple paths across the unstructured environment and uses entropy weighting for generating weights to calculate a multi-criteria cost with distance, risk, and soil trafficability. The paths generated by the proposed framework provide a better overall performance across the cost criteria and can be used as waypoints to navigate UGVs in off-road environments. Citation: S. Khatiwada, P. Murray-Tuite, M.J. Schmid, “Multi-Criteria
Khatiwada, SachetMurray-Tuite, PamelaSchmid, Matthias J
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