Browse Topic: Trajectory control

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Path Planning and Control Strategies for a Semi-Autonomous Light Commercial Vehicle Navigating and Avoiding Static Obstacles in Urban Environments2026-26-0134To be published on 01/16/2026
This paper presents the design and implementation of a Semi-Autonomous Light Commercial Vehicle (LCV) capable of following a person while performing obstacle avoidance in urban and controlled environments. The LCV leverages its onboard 360-degree view camera, RTK-GNSS, Ultrasonic sensors, and algorithms to independently navigate the environment, avoiding obstacles and maintaining a safe distance from the person it is following. The proposed system employs a path planning algorithm that generates a secondary lateral path originating from the primary driving path to navigate around static obstacles. A Behavior Planner is utilized to decide when to generate the path and avoid obstacles, ensuring timely and efficient navigation adjustments. The primary objective is to ensure seamless and safe navigation in environments where obstacles are present. The LCV's path tracking is achieved using a combination of Pure Pursuit and Proportional-Integral (PI) controllers. The Pure Pursuit controller
Ayyappan, Vimal Raj, Dhanopia, Rashmi, Ali, Ashpak, N, Ragesh, Sato, Hiromitsu
Algorithm development for AI driven Path planning using Augmented reality for navigating in Indian complex road scenarios2026-26-0038To be published on 01/16/2026
The road infrastructure in India has complex navigational challenges, with 82% roads being unstructured according to Ministry of Roads Transports and Highways (MoRTH). Decision-making becomes a challenge for drivers in unpredictable environments such as narrow roads or sometimes flooded roads and heavy traffic. In this paper, an augmented reality (AR) driver assistance algorithm has been proposed that improves awareness to safely maneuver in these conditions.The methodology for development and validation of this AR algorithm contains multiple steps. Firstly, extensive data collection is conducted using real time capture and benchmark datasets like Berkeley Deep Drive (BDD) and Indian Driving Dataset (IDD). Secondly, A machine learning model consisting of the collected data are labelled and trained to rightly identify the complex scenario. In third step, an AR navigation algorithm is developed, integrating these models to accurately detect floods and estimate road widths and provide
Anandaraj, Prem Raj, Sivakumar, Vishnu, Thanikachalam, Ganesh, L, Radhakrishnan, Motoki, Yaginuma, Selvam, Dinesh Kumar
Curvature-Continuous Trajectory Refinement for Autonomous Vehicles Using a Pure Pursuit Based Path Smoothing Framework2026-26-0135To be published on 01/16/2026
Path planning is a key element of autonomous vehicle navigation, allowing vehicles to calculate feasible paths in challenging environments. Algorithms such as Hybrid A*, Reeds-Shepp, and Dubins paths effectively generate collision-free paths but tend to create curvature discontinuities. These discontinuities result in sudden steering transitions, which create control instabilities, higher mechanical stress, and lower passenger comfort. To overcome these issues, this paper suggests a path-smoothing technique based on the pure-pursuit algorithm to produce curvature-continuous, distortion-free paths appropriate for real-world driving. This method utilizes the practical approach of the original path but removes sudden transitions that destabilize control. By ensuring smooth curvature, the vehicle undergoes fewer jerky steering actions, enhanced energy efficiency, less actuator wear, and improved high-speed tracking. Experimental verifications, such as real-time tests on autonomous vehicles
S, Shriniyathi, A, Josana, Edwin J, Joel, T, Akshayaa, M, Senthil Vel, Kumar, Vimal
Modeling on Self-Learning Driver Model under Extreme Driving Conditions2025-01-731512/31/2025
Ensuring the safe and stable operation of autonomous vehicles under extreme driving conditions requires the capability to approach the vehicle’s dynamic limits. Inspired by the adaptability and trial and error learning ability of expert human drivers, this study proposes a Self-Learning Driver Model (SLDM) that integrates trajectory planning and path tracking control. The framework consists of two core modules: In the trajectory planning stage, an iterative trajectory planning method based on vehicle dynamics constraints is employed to generate dynamically feasible limit trajectories while reducing sensitivity to initial conditions; In the control stage, a neural network enhanced nonlinear model predictive controller (NN-NMPC) is designed, which incorporates a self-learning mechanism to continuously update the internal vehicle model using trial-and-error data on top of mechanistic physical constraints, thereby improving predictive accuracy and path-tracking performance. By combining
Zhang, Xinjie, Xu, Long, Guo, Konghui, Zhuang, Ye, Hu, Tiegang, Mao, JingGang, Zeng, Qingqiang
Research on Control Strategy for Heterogeneous Traffic at Signalized Intersections2025-99-045312/10/2025
In order to reduce conflicts between vehicles at intersections and improve safety, an optimization model of traffic sequence allocation is studied and established for the heterogeneous traffic scenario of connected autonomous vehicles and manual vehicles. With the minimum safe traffic time as constraint, the right of way is allocated to vehicles according to the microscopic traffic characteristics of heterogeneous traffic flow fleet movement and the phase of signal lights, and the optimal trajectory planning control of each vehicle and evaluation indicators are established. A jointly simulation running environment is built using VISSIM and MATLAB. The simulation results indicate that at the micro level, collaborative control slows down the waiting time for manually driven vehicles and improves the utilization of green light travel time. At the macro level, as the penetration rate of connected autonomous vehicles increases, the sum of squares of vehicle acceleration gradually decreases
Yuan, Shoutong, Li, Zhiqiang, Liu, Tianyu, Yu, Zhengyang
A Research on Path Planning of Kelp Harvesting Vessel Based on Reciprocal Traversal2025-99-040712/10/2025
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Wang, Jie, Yang, Yue, Chen, Xin, Cui, Jiaxing
Research on Trajectory Planning for Urban Traffic Inspection Using Three-Dimensional Flight of UAVs2025-99-045212/10/2025
Objective:Methods:Results:Conclusion:
Sun, Ke, Wan, Qian, Liu, Qianqian, Li, Qiuling
Robust Lateral Trajectory Tracking of Intelligent Vehicles by Using Fuzzy Adaptive Dynamic Model Predictive Control10-10-02-000912/03/2025
To further improve the smoothness and robustness of lateral trajectory tracking for intelligent vehicles under complex operating conditions, this study proposes and experimentally validates a fuzzy adaptive dynamic model predictive control (FADMPC) strategy on the basis of model predictive control (MPC) framework. Thereinto, a three-degrees-of-freedom vehicle dynamics model serves as the predictive model, and a recursive least-squares algorithm with a forgetting factor is used to estimate tire cornering stiffness, thereby improving model fidelity. A whale optimization algorithm (WOA)–based adaptive horizon scheduler is devised to address the sensitivity of the prediction horizon to vehicle speed and road friction, and a fuzzy regulator adjusts the weight on the lateral displacement error in the objective function in real time. Hardware-in-the-loop tests on jointed and split-road surfaces show that compared with adaptive dynamic MPC, traditional MPC, and linear quadratic regulator, the
Teng, Fei, Jin, Liqiang, Wang, Junnian, Yang, Chen, Fan, Jiapeng, Qiu, Neng, Li, Andong, Zhou, Yanbo
Research on Urban Traffic Congestion Optimisation Strategies Based on V2X Technology2025-99-011711/11/2025
This study investigates urban traffic congestion optimisation strategies based on V2X technology. V2X technology (Vehicles and Internet of Everything) aims to alleviate urban traffic congestion, improve access efficiency, and reduce tailpipe emissions through real-time collection and fusion of traffic data to optimise traffic signal control and path planning. The efficacy of the optimisation strategies under different V2X penetration rates is evaluated by conducting multi-factor orthogonal experiments in different typical congestion scenarios. The experimental results show that the V2X-based signal optimisation, path induction, and event response combination strategies exhibit significant optimisation effects in all three scenarios: node bottleneck, corridor congestion, and event induction. Under the condition of 100% penetration, the combined strategy reduces delay by 41.9% in the node bottleneck scenario, improves accessibility by 28.1% in the corridor congestion scenario, and
Xi, Chaohu, Li, Jiasheng, Qu, Fengzhen, Liu, Hongjun, Liu, Xiaorui, Wang, Chunpeng
Multimodal Intelligent Transportation Data Fusion and Application Methods in Heavy-Duty Vehicles2025-99-013911/11/2025
Heavy-duty commercial vehicles (HDCVs) are the key mobile nodes in intelligent transportation systems (ITS). However, their complex operating conditions and the diversity of data sources (such as road conditions, driver behavior, traffic signals, and on-board sensors) present considerable difficulties for accurately estimating the state and perceiving the environment using a single modality of data. This requires effective multi-modal data fusion to enhance the control and decision-making capabilities of HDCVs. This paper addresses this need by proposing a customized multi-modal intelligent transportation data fusion framework for intelligent HDCVs. This paper presents a solution for establishing a multi-modal intelligent transportation data collection platform, including real-scene collection methods and simulation scene collection methods based on the SUMO-MATLAB joint simulation platform. Through three representative case studies, the application methods of multi-modal traffic data
Chen, Zhengxian, Wang, Shaoqi, Jiang, Huiming, Zhou, Fojin, Wang, Mingqiang, Li, Jun
Intelligent Decision-Making and Trajectory Planning of Semi-Trailer Trains Under Multi-Vehicle Interference2025-99-012511/11/2025
Semi-trailer trains are the main force of highway freight. In a complex environment with multiple vehicles, accidents are easily caused by complex structures and driver operation problems. Intelligent technology is urgently needed to improve safety. In view of the shortcomings of existing research on its dedicated models and algorithms, this paper studies the intelligent decision-making and trajectory planning of semi-trailer trains under multiple vehicles. A local trajectory planning method based on global path planning and Frenet coordinate decoupling based on the improved A* algorithm is proposed. The smooth weight transition function and B-spline curve are introduced to optimize the global path. The polynomial function is combined with the acceleration rate to optimize the local trajectory. TruckSim, Prescan and Simulink are used to build a joint simulation platform for multi-condition verification. The simulation results show that the search efficiency of the improved A* algorithm
Song, Zeyuan, Geng, Shuai
Machine Learning Based off-Road Vehicle Turn Identification Using Vehicle & GPS Parameters2025-28-034411/06/2025
Identification of different types of turns during field operation of off-road vehicles is critical in the overall vehicle development as it is helpful in identifying & optimizing machine performance, correct duty cycle, fuel economy, stability analysis, accurate path planning, customer usage pattern & designing the critical components, etc. In this study, a machine learning (ML) based methodology has been developed to detect the off-road vehicle turns using vehicle & GPS parameters. Three most common types of off-road vehicles turn conditions e.g., Straight line, Bulb turn, and Three-Point turn have been considered. Different vehicle parameters (like latitude & longitude, compass bearing, yaw rate, vehicle speed, swash plate angle, engine speed, percent load at vehicle speed, raise lower front & PTO channels) generated during field test have been used here. These vehicle parameters are further processed, analysed and used in ML learning model building. Four ML models e.g., SVM, K-NN
Rai, Rohit, Gangsar, Purushottam
A Hybrid Framework for Kinematic Vehicle Prediction: YOLOv11 with Unscented Kalman Filter12-09-02-001310/22/2025
In the context of intelligent transportation systems and applications such as autonomous driving, it is essential to predict a vehicle’s immediate future states to enable precise and timely prediction of vehicles’ movements. This article proposes a hybrid short-term kinematic vehicle prediction framework that integrates a novel object detection model, You Only Look Once version 11 (YOLOv11), with an unscented Kalman filter (UKF), a reliable state estimation technique. This study provides a unique method for real-time detection of vehicles in traffic scenes, tracking and predicting their short-term kinematics. Locating the vehicle accurately and classifying it in a range of dynamic scenarios is achievable by the enhanced detection capabilities of YOLOv11. These detections are used as inputs by the UKF to estimate and predict the future positions of the vehicles while considering measurement noise and dynamic model errors. The focus of this work is on individual vehicle motion prediction
Pahal, Sudesh, Nandal, Priyanka
Logistics and Distribution Centre Site Selection under Dynamic Spare Parts Demand - Path Optimisation2025-99-007810/17/2025
Aiming at the dynamic customer demand for multiple products in different cycles, with the lowest total cost of the distribution system as the goal, taking into account distribution centre capacity, vehicle loading and other resource constraints, vehicle loading and other resource constraints, we constructed a two-layer objective planning model of distribution centre siting-vehicle path optimization. The upper model is solved by Gurobi to obtain the distribution centre location and customer division scheme, the greedy algorithm will be applied to solve the initial vehicle path planning, and then uses the particle swarm algorithm for optimisation to obtain the corresponding location scheme and vehicle scheduling scheme. Taking an automotive aftermarket spare parts data as an example, the distribution centre site selection and vehicle path scheme are determined in t1and t2 cycles respectively, and the findings indicate that the model can be effective in reducing the possible waste of the
Zhu, Junrong, Zhang, Liping
Research on Coastal Ship Evacuation Path Planning using Improved Artificial Potential Field Method2025-99-000410/17/2025
It is essential to plan ship refuge paths for safety of ship and reduction of accident loss considering many marine accidents have happened. This paper presents improved artificial potential field (APF) method for generating ship refuge paths planning along the coast. Relying on the APF model, this new approach takes into account the challenges brought by dynamic surroundings and obstacles in the coastal waters. The introduced model develops a multi-level potential field organization, the influence of the gravitational and the repulsive forces can be adjusted adaptively based on the real time environment data. Simultaneously, adaptive algorithm is integrated to adaptively modify the parameters of potential field and improve the convergence speed of the algorithm and avoid the popular local minimum problem in traditional APF methods. Additionally, the model includes a risk ranking functionality that provides prioritization of the evacuation route according to the distance of the ship to
Bai, Chunjiang, Gou, Zhijian, Sui, Hongbin
Research on collision Avoidance Path Planning of Inland Waterway Vessels Based on Deep Learning Technology2025-99-001510/17/2025
How to realize the intelligent collision avoidance of inland waterway ships has become a hot issue in the field of transportation. The navigation status, position information and speed of inland vessels can be obtained by using the shipborne Beidou terminal and AIS, so as to realize the real-time monitoring of the ship’s operation status and the real-time optimization of collision avoidance path planning. In the process of track classification and prediction, it is necessary to use deep learning algorithms to train and learn historical track data, so as to generate a model that can accurately predict future tracks, and make collision avoidance path planning decisions on this basis, so as to realize the intelligence of water traffic organization and ship collision avoidance.
Liu, Xingchen, Cui, Jianzhang, Kong, Lingqi
Motion Planning Algorithm for Autonomous Driving Based on Trajectory Prediction2025-99-004710/17/2025
To solve the problems of trajectory prediction and obstacle avoidance of self-vehicles in autonomous driving, a obstacle avoidance algorithm that combines trajectory prediction and vehicle motion planning is proposed. Firstly, in this paper, Unscented Kalman filter and constant acceleration model, namely UKF + CA, as well as Hidden Markov model, namely HMM, are combined together. Predict the trajectory of the vehicle in front and integrate the prediction results obtained by these two methods, which can improve the accuracy of the prediction. Then, in the Frenet coordinate system, this paper adopts the methods of dynamic programming and quadratic programming to generate the planning trajectory of the self-aircraft. After that, this paper conducts collision detection between the fusion trajectory of the preceding vehicle and the planning trajectory of the self-vehicle. If there is a risk of collision, a virtual obstacle will be generated and the path will be re-planned to avoid the
Cao, Zheng, Shen, Yong-Feng, Hu, Hao-Dong, Ouyang, Le-Wen
Coordinated Strategy of Trajectory Tracking and Stability Control for Autonomous Vehicles with Switched Envelope and Composite Evaluation10-10-01-000310/13/2025
Trajectory tracking and lateral stability under extreme conditions are critical yet conflicting control objectives due to nonlinear tire dynamics and road adhesion limitation, where accurate characterization of vehicle dynamics for each objective is essential to enable coordinated performance. This article proposes a coordinated control strategy based on switched envelope and composite evaluation to improve both tracking accuracy and stability. Unlike previous stability envelope methods that rely solely on the vehicle’s rear tire saturation boundary to prevent instability, the switched envelope approach incorporates both front and rear tire saturation boundaries to simultaneously mitigate steering loss and instability in trajectory tracking. A critical steering angle, derived from tire slip dynamics and phase plane stability analysis, is formulated as the switching criterion. Additionally, a composite stability evaluation is developed by combining a future disturbance resistance index
Shi, Wenbo, Wang, Junlong, Ding, Haitao, Xu, Nan
A Control Strategy of Path Tracking for Unmanned Mining Truck Based on Model Predictive Control and Considering Steering Delay10-09-04-003708/22/2025
To achieve accurate and stable path tracking for unmanned mining trucks in the face of changing paths and response delays in steering, this study raised a lateral control strategy for unmanned mining trucks based on MPC and considering steering delay response characteristics. Under the basis of deriving the state space equation from the commonly used two degrees of freedom truck dynamics model, this method introduces the dynamic relationship between steering angle issuance and actual response to form an augmented form of state vector to overcome the control instability caused by steering response delay. Then, based on the MPC method, a constrained objective function is constructed to solve for the optimal control law. In response to the problem of inaccurate selection of prediction and control time domains, this article proposes an adaptive selection method for prediction and control time horizon based on a modified particle swarm optimization (MPSO) algorithm, which obtains the
Mao, Libo, Wu, Guangqiang, Gui, Yuhui
Adaptive Path Planning and Control of Autonomous Sweeping Vehicle Considering the Error of Perception10-09-04-003508/19/2025
This article presents a path planning and control method for a cost-effective autonomous sweeping vehicle operating in enclosed campus. First, to address the challenges from perception, an effective obstacle filtering algorithm is proposed, considering the elimination of false detection and correction of object position. Based on it, the adaptive sampling–based path planner and pure pursuit controller are developed. Not only an adaptive cost-weighting mechanism is introduced by TOPSIS algorithm to determine the desired trajectory as a multi-objective optimization problem, but also the adaptive preview distance is designed according to the trajectory curvature and vehicle state. The real-vehicle tests are implemented in typical scenario. The results show that the 87.8% effective edge-following rate is achieved in curved paths, and 22.93% cleaning coverage is improved for cleaning coverage. Therefore, the proposed method is effective and reliable for cost-effective autonomous sweeping
Lei, WuKun, Yang, Bo, Pei, Xiaofei, Zhang, Yang, Zhou, HongLong
Hybrid Data- and Physical Model-driven Safe and Intelligent Motion Planning and Control for Autonomous VehiclesEPR202501407/18/2025
Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and
Zheng, Ling
Data-Driven Analysis of Vehicle Lateral Control in the Country Road Domain2025-01-026807/02/2025
The optimization and further development of automated driving functions offer significant potential for reducing the driver's workload and increasing road safety. Among these functions, vehicle lateral control plays a critical role, especially with regard to its acceptance by end customers. Significant development efforts are required to ensure the effectiveness and reliability of this aspect in real-world conditions. This work focuses on analyzing lateral vehicle control using extensive measurement data collected from a dedicated vehicle fleet at the Institute of Automotive Engineering at the Technical University of Braunschweig. Equipped with state-of-the-art measurement technology, the fleet has driven several hundred thousand kilometers, allowing for the collection of detailed information on vehicle trajectories under various driving conditions. A total of 93 participants, aged between 20 and 43 years, contributed to the dataset. These measurements have been classified into
Iatropoulos, Jannes, Panzer, Anna, Arntz, Martin, Prueggler, Adrian, Henze, Roman
Partial Experimental Validation of Generalized n-Trailer Articulated Vehicle Kinematics2025-01-027207/02/2025
Control-oriented models for vehicle systems are necessary to develop motion planning and path tracking controllers for active safety system development. While being mathematically elegant and simple enough for control design, such models must represent real-world phenomena associated with the vehicle’s kinematic and dynamic behavior. Specifically, articulated vehicles suffer from peculiarities like rearward amplification and offtracking in their kinematic behavior that are not found in single-unit passenger vehicles. In this paper, an iterative kinematic modeling algorithm for articulated truck-trailer vehicles with an arbitrary number of vehicle units having an arbitrary number of axles on each vehicle unit is evaluated using driver input data collected from an experimental passenger vehicle on eight real-world scenarios. The experimental vehicle is considered as the tractor vehicle unit for a simulation study in which multiple trailers of various geometries are considered. The yaw
Singh, Yuvraj, Giuliani, Pio Michele, Jayakumar, Adithya, Javed, Nur Uddin, Tan, Shengzhe, Rizzoni, Giorgio
The Automation Concept of the U-Shift II Vehicle2025-01-028107/02/2025
Autonomous driving technology enables new and innovative driverless vehicle concepts to emerge, like U-Shift. Designed from the ground up, the U-Shift II platform, called driveboard, exemplifies the advantages of separating a vehicle’s driving capability from the intended transportation task. It allows different so-called capsules, such as public transport or cargo, to be transported using the same U-shaped driving platform. The driveboard can change the capsules autonomously, thus providing high flexibility for fleet operators. This novel approach introduces new challenges to the task of autonomous driving. On one hand, changing sensor and vehicle configurations, e.g., when transporting a capsule with its own sensors to compensate for occlusions of the driveboard sensors by the capsule itself, requires an adaptive approach to environmental perception. On the other hand, different environments and driving tasks, as well as the augmented motion capabilities of the driveboard, require
Buchholz, Michael, Wodtko, Thomas, Schumann, Oliver, Authaler, Dominik
A Generalized Formulation for Safe Obstacle Avoidance Maneuvers in Autonomous Vehicles2025-01-028507/02/2025
This paper deals with autonomous vehicle trajectory planning for avoidance maneuver. It introduces a trajectory planning approach that allows for static obstacle avoidance maneuvers. Specifically, this study proposes a generalized geometric formulation based on Sigmoid functions in order to generate a smooth path that guides the vehicle on a lateral deviation and returns to the initial lane. In addition, the method considers various geometrical and dynamic constraints to ensure vehicle stability while taking into account a safety area around the obstacle. The algorithm validation is conducted on the professional CarMaker simulator by associating the path generation module with a robust lateral tracking controller. The results demonstrate the effectiveness of the proposed planning method in the field of autonomous driving vehicle control.
Vigne, Benoit, Giuliani, Pio Michele, Orjuela, Rodolfo, Basset, Michel
Optimal Geometrical Planning of Autonomous Vehicle Trajectory2025-01-503706/10/2025
Trajectory planning is a major challenge in robotics and autonomous vehicles, ensuring both efficient and safe navigation. The primary objective of this work is to generate an optimal trajectory connecting a starting point to a destination while meeting specific requirements, such as minimizing travel distance and adhering to the vehicle’s kinematic and dynamic constraints. The developed algorithms for trajectory design, defined as a sequence of arcs and straight segments, offer a significant advantage due to their low computational complexity, making them well-suited for real-time applications in autonomous navigation. The proposed trajectory model serves as a benchmark for comparing actual vehicle paths in trajectory control studies. Simulation results demonstrate the robustness of the proposed method across various scenarios.
Soundouss, Halima, Msaaf, Mohammed, Belmajdoub, Fouad
Control Trajectory Optimization of Electric Vehicle Heat Pump-Based Cabin Heating System2025-01-814404/01/2025
Optimal control of battery electric vehicle thermal management systems is essential for maximizing the driving range in extreme weather conditions. Vehicles equipped with advanced heating, ventilation and air-conditioning (HVAC) systems based on heat pumps with secondary coolant loops are more challenging to control due to actuator redundancy and increased thermal inertia. This paper presents the dynamic programming (DP)-based offline control trajectory optimization of heat pump-based HVAC aimed at maximizing thermal comfort and energy efficiency. Besides deriving benchmark results, the goal of trajectory optimization is to gain insights for practical hierarchical control strategy modifications to further improve real-time controllers’ performance. DP optimizes cabin inlet air temperature and flow rate to set the trade-off between thermal comfort and energy efficiency while considering the nonlinear dynamics and operating limits of HVAC system in addition to typically considered cabin
Cvok, Ivan, Deur, Josko
CL-infoRRT Collision-Avoidance Trajectory Planning for Autonomous Vehicle2025-01-802704/01/2025
To ensure the safety and stability of road traffic, autonomous vehicles must proactively avoid collisions with traffic participants when driving on public roads. Collision avoidance refers to the process by which autonomous vehicles detect and avoid static and dynamic obstacles on the road, ensuring safe navigation in complex traffic environments. To achieve effective obstacle avoidance, this paper proposes a CL-infoRRT planning algorithm. CL-infoRRT consists of two parts. The first part is the informed RRT algorithm for structured roads, which is used to plan the reference path for obstacle avoidance. The second part is a closed-loop simulation module that incorporates vehicle kinematics to smooth the planned obstacle avoidance reference path, resulting in an executable obstacle avoidance trajectory. To verify the effectiveness of the proposed method, four static obstacle test scenarios and four RRT comparison algorithms were designed. The implementation results show that all five
Wu, Wei, Lu, Jun, Zeng, Dequan, Yang, Jinwen, Hu, Yiming, Yu, Qin, Wang, Xiaoliang
Tube-MPC Based Path Tracking for I Vehicle Considering Data-Driven Stable Region and External Disturbances2025-01-826204/01/2025
Path tracking is a key function of intelligent vehicles, which is the basis for the development and realization of advanced autonomous driving. However, the imprecision of the control model and external disturbances such as wind and sudden road conditions will affect the path tracking effect and even lead to accidents. This paper proposes an intelligent vehicle path tracking strategy based on Tube-MPC and data-driven stable region to enhance vehicle stability and path tracking performance in the presence of external interference. Using BP-NN combined with the state-of-the-art energy valley optimization algorithm, the five eigenvalues of the stable region of the vehicle β−β̇ phase plane are obtained, which are used as constraints for the Tube-MPC controller and converted into quadratic forms for easy calculation. In the calculation of Tube invariant sets, reachable sets are used instead of robust positive invariant sets to reduce the calculation. Simulation results demonstrates that the
Zhang, Haosen, Li, Yihang, Wu, Guangqiang
Vehicle Motion Management - A Model Predictive Control Approach to Realize Holistic Redundancy to Enable Actuator Fail Operational Autonomous Driving2025-01-881204/01/2025
Since the introduction of ABS (1978), TCS (1986) and ESC (1995) in series production, the number of modern vehicle dynamics control functions and advanced driver assistance systems (ADAS) has been continuously increasing. Meanwhile, many functions are available that influence vehicle motion (vehicle dynamics). Since these are only partially and not hierarchically coordinated, the control of vehicle motion is still suboptimal. Current megatrends (automated driving, electromobility, software-defined vehicles) and new key technologies (steer-by-wire, brake-by-wire, domain-based E/E architectures) lead to an increasing number of electrified, motion-relevant components being introduced into series production. These components enable the development of an integrated chassis control (ICC) that controls all motion-relevant components, networks them with each other and coordinates them holistically to optimally control the vehicle motion regarding an adjustable desired driving behavior. Vehicle
Wielitzka, Mark, Ahrenhold, Tim, Vocht, Moritz, Rawitzer, Jonas, Schrader, Jonas
Research on Sampling-Based Motion Planning and Control in Spiral Ramp Scenarios of Underground Parking Garages2025-01-803904/01/2025
The slope and curvature of spiral ramps in underground parking garages change continuously, and often lacks of predefined map information. Traditional planning algorithms is difficult to ensure safety and real-time performance for autonomous vehicles entering and exiting underground parking garages. Therefore, this study proposed the Model Predictive Path Integral (MPPI) method, focusing on solving motion planning problems in underground parking garages without predefined map information. This sample-based method to allows simultaneous online autonomous vehicle planning and tracking while not relying on predefined map information,along with adjusting the driving path accordingly. Key path points in the spiral ramp environment were defined by curvature, where reducing the dimensionality of the sampling space and optimizing the computational efficiency of sampled trajectories within the MPPI framework. This ensured the safety and computational speed of the improved MPPI method in motion
Liu, Zuyang, Shen, Yanhua, Wang, Kaidi
Safe Deployment of AI and ML Based Software and Algorithms in ADAS Systems2025-01-807104/01/2025
As the autonomy of ADAS features are moving from SAE level 0 autonomy to SAE level 5 autonomy of operation, reliance on AI/ML based algorithms in ADAS critical functions like perception, fusion and path planning are increasing predominantly. AI/ML based algorithms offer exceptional performance of the ADAS features, at the same time these advanced algorithms also bring in safety challenges as well. This paper explores the functional safety aspects of AI/ML based systems in ADAS functions like perception, object fusion and path planning, by discussing the safety requirements development for AI/ML systems, dataset safety life cycle, verification and validation of AI systems, and safety analysis used for AI systems. Among all the safety aspects listed above, emphasis is put on dataset safety lifecycle as that is not only the most important element for training ML based algorithms for ADAS usage, but also the most cumbersome and expensive. The safety characteristics associated with dataset
Mudunuri, Venkateswara Raju, Almasri, Hossam, Fan, Hsing-Hua, Chandrasekaran, Mukund
Trajectory Planning and Optimal Autonomous Steering Control Design for Towbarless Aircraft Taxiing System2025-01-829304/01/2025
An efficient and safe aircraft scheduling scheme is of great significance to the construction of smart airports. The towbarless aircraft taxiing system (TLATS) is a common dispatching method, which is composed of the towbarless towing vehicle (TLTV) and the aircraft. The system’s trajectory planning and autonomous steering control are being researched in order to improve steering accuracy, dispatching efficiency, and safety. In this article, the towbarless aircraft taxiing system is transformed into tractor-trailer system, the kinematic model and the dynamic model of the aircraft-tractor are established. Taking TLTV as a virtual subsystem of TLATS, and it is regarded as the controlled object of path planning and tracking. In response to the operational requirements of TLTV, an advanced A-star(A*) path planning algorithm is proposed to perform collision avoidance and turn radius restrictions during path planning resulting in a reference path for TLATS. Considering the estimation
Zhu, Hengjia, Zhao, Zhouqiao, Xu, Yitong, Zhang, Wei
Influence of Prediction Horizon on Trajectory Optimization for Autonomous Vehicle Maneuvers2025-01-830904/01/2025
The advent of autonomous vehicles (AVs) marks a revolutionizing transformation in transportation, with the potential to significantly enhance safety and efficiency through advanced trajectory planning and optimization capabilities. A crucial component in realizing these benefits is the use of optimization-based control strategies for real-time path planning. Among these, model predictive path integral (MPPI) control algorithms stand out as a sampling-based stochastic control method, offering precise control in dynamic environments through random sampling. While the MPPI control has shown promising results, there has been limited investigation into the effects of different prediction horizon times on control performance of these algorithms. This paper seeks to address this gap by proposing a multi-input MPPI control method for AVs using a single-track vehicle dynamic model. Our research focuses on the influence of various prediction horizon times on trajectory optimization during lane
Yang, Yanwen, Negash, Natnael, Yang, James
Path Planning and Following Control of All-Wheel Steering Vehicle with Dual Modular Chassis2025-01-879304/01/2025
A large-scale logistics transport vehicle composed of two skateboard chassis is investigated in this paper. This unmanned vehicle with dual-modular chassis (VDUC) is suitable for transporting varying size of goods. The two chassis can be used jointly or driving separately as needed, which enhancing the reconfigurability of transport vehicle. Considering the road environment uncertainty and the rollover safety problem associated with large transport vehicle, this paper proposes the path planning of VDUC using the Artificial Potential Field(APF)+Model Predictive Control(MPC) while incorporating the rollover stability index. Due to the independent operation of the two modular chassis, based on the hierarchical control approach, the path following controller of the two modular chassis are designed separately according to the vehicle’s planned path. Distributed model predictive control is applied to coordinate the front and rear modular chassis, so it can realize the path following for the
Liu, Zuyang, Shen, Yanhua, Wang, Kaidi, wang, Haoshuai
Biofidelity Evaluation of AC-HUMs Pedestrian Model Based on Generic Sedan Buck Model2025-01-874304/01/2025
Objective: This study aims to evaluate the biofidelity of the Advanced Chinese Human Body Model (AC-HUMs) by utilizing a generic sedan buck model and post-mortem human surrogates (PMHS) test data. Methods: The boundary conditions of the simulation were derived from the PMHS test with the buck vehicle. The methodology involved the pose adjustment of the upper and lower extremities of AC-HUMs, executed through a pre-simulation approach. Subsequently, a 200 milliseconds whole body pedestrian crash simulation was conducted using the buck vehicle and the AC-HUMs pedestrian model. The trajectories of AC-HUMs during the period from initial position to head impact were recorded, including the Head CG, T1, T8 and pelvis. Based on the knee joint, the corridors of trajectories from the PMHS test were scaled to match the Chinese 50th percentile male to evaluate the biofidelity of AC-HUMs's kinematic response. Furthermore, the biomechanical responses were compared with the PMHS tests, including
Qian, Jiaqi, Wang, Qiang, Liu, Yu, Wu, Xiaofan, Huida, Zhang, Bai, Zhonghao
MPC-Based Trajectory Tracking Controller for Autonomous Vehicles2025-01-802604/01/2025
Trajectory tracking control is a critical component of the autopilot system, essential for achieving high-performance autonomous driving. This paper presents the design of a stable, reliable, accurate, fast, and robust trajectory tracking controller. Specifically, a lateral and longitudinal trajectory tracking controller based on a linear parameter time-varying model predictive control (LPV-MPC) framework is designed. Firstly, a three-degree-of-freedom vehicle dynamics model and a tracking error model are established. Secondly, a multi-objective function and constraints considering tracking accuracy and lateral stability are formulated, and the quadratic programming (QP) method is employed to solve the optimization problem. Finally, PID speed tracking control is introduced in the longitudinal control scheme for comparison with the proposed MPC longitudinal speed control. A step velocity tracking test validates the effectiveness of the MPC speed tracking controller. In the lateral
Pan, Shicong, Lu, Jun, Yu, Yinquan, Zeng, Dequan, Yang, Jinwen, Hu, Yiming, Jiang, Zhiqiang, Liu, Dengcheng
Stability-Based Coordinated Control for Distributed Drive Electric Vehicle Trajectory Tracking and Handling Stability2025-01-830704/01/2025
To address the issue of poor yaw stability in distributed drive electric vehicles under extreme trajectory tracking conditions, this paper proposes a novel control approach that coordinates upper-layer trajectory tracking and stability control with lower-layer active front steering (AFS) and direct yaw moment control (DYC). Firstly, a stability domain boundary is defined in the β−β̇phase plane, and the instability factor is derived based on boundary line characteristics. This factor is used as a weight in the objective function to establish a model predictive control (MPC) for trajectory tracking and handling stability, thereby adjusting the control target weights for both objectives. Secondly, fuzzy logic is used to change the boundary of the phase plane transition field according to the vehicle state to dynamically adjust the intervention timing of the stability control, while AFS and DYC control are used to modify the front wheel steering angle and yaw moment control in the MPC
Dou, Jingyang, Wu, Jinglai, Zhang, Yunqing
A Path Tracking Control Method Considering Vehicle Stability and Road Curvature2025-01-803804/01/2025
The current research landscape in path tracking control predominantly focuses on enhancing tracking accuracy, often overlooking the critical aspect of passenger comfort. To address this gap, we propose a novel path tracking control method that integrates vehicle stability indicators and road curvature variations to elevate passenger comfort. The core contributions are threefold: firstly, we conduct comprehensive vehicle dynamics modeling and analysis to identify key parameters that significantly impact ride comfort. By integrating human comfort metrics with vehicle maneuverability indices, we determine the optimal range of dynamics parameters for maximizing passenger comfort during driving. Secondly, inspired by human driving behavior, we design a path tracking controller that incorporates an anti-saturation algorithm to stabilize tracking errors and a curvature optimization algorithm to mimic human driving patterns, thereby enhancing comfort. Lastly, comparative simulations with two
Lu, Jun, Zeng, Dequan, Hu, Yiming, Wang, Xiaoliang, Liu, Dengcheng, Jiang, Zhiqiang
Research on Automatic Emergency Braking Pedestrian System Considering Road Adhesion Coefficient10-09-03-002203/08/2025
To further optimize the automatic emergency braking for pedestrian (AEB-P) control algorithm, this study proposes an AEB-P hierarchical control strategy considering road adhesion coefficient. First, the extended Kalman filter is used to estimate the road adhesion coefficient, and the recursive least square method is used to predict the pedestrian trajectory. Then, a safety distance model considering the influence factor of road adhesion coefficient is proposed to adapt to different road conditions. Finally, the desired deceleration is converted into the desired pressure and desired current to the requirements of the electric power-assisted braking system. The strategy is verified through the hardware-in-the-loop (HIL) platform; the simulation results show that the control algorithm proposed in this article can effectively avoid collision in typical scenarios, the safe distance of parking is between 0.61 m and 2.34 m, and the stop speed is in the range of 1.85 km/h–27.64 km/h.
Wang, Zijun, Wang, Liang, Ma, Liang, Sun, Yong, Li, Chenghao, Yang, Xinglong
Research and Simulation Implementation of Self-Driving Vehicle Trajectory Optimization in Networked Intersection Environment2025-01-719402/21/2025
Nowadays, the rapidly developing Connected and Autonomous Vehicle (CAV) provides a new mode of intersection vehicle cooperative control, which can optimize vehicle trajectories and signal phases in real time and reduce intersection delays through the advantages of vehicle-road cooperative information interaction and the high controllability of CAV. In this paper, the intersection of Jintong West Road and Guanghua Road in Beijing is taken as the research object, and the vehicle trajectory constraints, acceleration constraints, speed constraints, safe driving constraints, signal switch constraints and traffic signal control constraints are set up with the minimization of traffic delay as the objective function. The DQN deep reinforcement learning network is constructed based on vehicle states, vehicle actions, reward functions, and update rules, and starts learning and updating to generate the target network. Then, SUMO software is used to simulate and test and compare the trajectory
Xu, Yuting, Zhang, Yong, Wu, Xianyu
Research on Taxiing Path Planning of Hub Airport Based on Dijkstra Algorithm2025-01-719802/21/2025
Aiming at the problem of insufficient capacity of taxiways in hub airports, which combine the safety interval, conflict resolution and fair principles, a taxiway planning model is established by taking the shortest taxiway as the optimisation goal, considering fuel consumption and exhaust emissions. Dijkstra algorithm is used to transform the taxiing path into an adjacency matrix, and conflict resolution is carried out in a weighted way. Under the premise of ensuring zero conflict of taxiways, the total taxiing distance is reduced. Based on actual operational data from a hub airport in China, the results show that the proposed taxiing path planning method is feasible, shortening the aircraft taxiing distance and improving the surface taxiing efficiency.
Feng, Bocheng, Qi, Xinyue, Zhang, Hongbin
Design of a Ship Path Planning Scheme Incorporating Wind and Current Effects with COLREGS Compliance2025-01-717702/21/2025
Path planning algorithms are critical technologies for intelligent ship systems, as scientifically optimized paths enable safe navigation and efficient avoidance of waterborne obstacles. To address the limitations of current ship path planning models, which often fail to adequately consider the combined effects of wind, current, and the International Regulations for Preventing Collisions at Sea (COLREGS), this study proposes an enhanced path planning method. The method integrates environmental factors, such as wind and current, and COLREGS into an improved Artificial Potential Field(APF) framework. Specifically, the influence of wind and current is modeled as "environmental forces," while the navigation constraints imposed by COLREGS are transformed into virtual obstacles, generating corresponding repulsive forces to refine the algorithm. Simulation experiments conducted under both single-ship and multi-ship scenarios validate the feasibility and effectiveness of the proposed approach
Shangqing, Feng, Jinli, Xiao, Langxiong, Gan, Geng, Chen, Hui, Li, Guanliang, Zhou
Intelligent Vehicle Trajectory Tracking Control Based on Variable Universe Fuzzy Rule Speed Planning and Piecewise Preview Model Prediction02-18-01-000602/06/2025
Intelligent vehicles can utilize a variety of sensors, computing, and control technologies to autonomously perceive the environment and make decisions to achieve safe, efficient, and automated driving. If the speed planning of intelligent vehicles ignores the vehicle dynamics state, it leads to unreasonable planning speed and is not conducive to improving the accuracy of trajectory tracking control. Meanwhile, trajectory tracking usually does not consider the road and speed information beyond the prediction horizon, resulting in poor tracking precision that is not conducive to improving driving comfort. To solve these problems, this study proposes a new longitudinal speed planning method based on variable universe fuzzy rules and designs the piecewise preview model predictive control (PPMPC) to realize the vehicle trajectory tracking. First, the three-degrees-of-freedom vehicle dynamics model and trajectory tracking model are established and verified. Then, the variable universe fuzzy
Zhang, Jie, Teng, Shipeng, Gao, Jianjie, Zhou, Xingxing, Zhou, Junchao
Vibration Suppression of a Flexible Appendage and Pitch Angle Fuzzy Regulation of a Spacecraft01-18-01-000402/05/2025
In this work, the large-angle rotational movement and vibration suppression of a flexible spacecraft are carried out based on an adjustable system. First the spacecraft model is transformed into a canonical affine control form, then two fuzzy systems are used: The first (of Takagi–Sugeno type) estimates the feedback linearization control law as a whole, while the second (of Mamdani type) adjusts and stabilizes the control parameters using the gradient descent technique and based on the minimization of the control error rather than the tracking error. Stability results are presented in terms of Lyapunov’s theory, and simulation tests illustrate the significant transient robustness of the closed-loop system against perturbations, the accurate trajectory control, and vibration suppression of the flexible spacecraft. Consequently, as will be shown later, the error will stay confined and converges quickly to zero, confirming the smoothing property of the proposed method using fuzzy logic
Bahita, Mohamed
Multi-Objective Optimization for Lane-Change Decision and Trajectory Planning in Autonomous Buses2025-01-703701/31/2025
To improve the real-time performance and safety of intelligent bus lane-changing and obstacle avoidance in complex road environments, this study proposes a multi-objective optimization algorithm called LMCTS. L-MCTS integrates a lane-changing benefit model, an LSTM network, and Monte Carlo Tree Search. First, the NGSIM dataset was utilized to filter lane-changing intention points and surrounding traffic flow information, and classification rules were established to process lane-changing behaviors. Based on these decision outcomes, a multi-objective trajectory planning method was designed, taking into account factors such as comfort, safety, and smoothness. The proposed algorithm was validated on the CARLA simulation platform and compared with traditional MCTS and DP+QP algorithms. Results indicated that, in actual driving scenarios, the safety evaluation of L-MCTS improved by 10.71% compared to MCTS and by 17.72% compared to DP+QP. Additionally, L-MCTS enhanced comfort by 4.94% over
Jin, Jianfeng, Song, Kang, Xie, Hui, Yan, Long
Vehicle Collision Avoidance Path Planning Based on Trajectory Prediction12-08-03-002901/07/2025
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 Long, Zhang, Lei, Li, Peng Kun, Xie, Ru, Wang, Qing, Li, Ran Ran
Research on the Intelligent Vehicle Trajectory Tracking Control Based on Optimal Preview Distance02-18-01-000501/03/2025
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, Kehan, Zhang, Huanhuan, Hu, Shengli, Ning, Qianjia
Simulation Analysis of Multi-Objective Functions in Mobile Robot Navigation Based on Enhanced Deep Q-Network Algorithm2024-01-511012/24/2024
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, Vengatesan, Alagumalai, Vasudevan, Rajendran, Sundarakannan
Low-Level Data Fusion between Camera and Automotive RADAR for Vehicle and Pedestrian Detection Using nuScenes Database2024-36-006412/20/2024
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 Habib, Teixeira, Evandro Leonardo Silva, Silva, Rafael Rodrigues
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