Path Planning Of Autonomous Vehicles Research Articles

The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

The crucial dynamic path planning of autonomous vehicles is achieved via obstacle avoidance path planning technology. The reduction of the tire adhesion coefficient on icy and snowy roads (ISRs) increases the difficulty of autonomous vehicles’ control. In this paper, the driving characteristics of vehicles on ISRs are established, and the artificial potential field function is introduced to avoid collision risk when planning a path. A dynamic path planning algorithm for autonomous vehicles based on the artificial potential field (APF) is established. The adjustment factor is added to the gravitational potential field, and a judgment coefficient is added to the repulsive potential field to improve the artificial potential field function, based on the low adhesion of vehicles on ISRs. Moreover, a path with a continuous curvature is generated to achieve the driving comfort and driving safety of the planned path via trajectory smoothing. By establishing the Carsim/Simulink co-simulation platform, the effectiveness of dynamic path planning for autonomous vehicles under different algorithms and different obstacle models is compared. The results show that the improved APF algorithm has an obvious effect on the smoothness of the path and the reduction of the curvature mutation and can generate a safe and efficient path on icy and snowy roads. The dynamic obstacle avoidance of the improved APF algorithm improves the pre-judgment accuracy of the collision risk assessment of autonomous vehicles and shows the superiority of the improved algorithm.

Backup Plan Constrained Model Predictive Control with Guaranteed Stability

This paper proposes and evaluates a new safety concept called backup plan safety for path planning of autonomous vehicles under mission uncertainty using model predictive control (MPC). Backup plan safety is defined as the ability to complete an alternative mission when the primary mission is aborted. To include this new safety concept in control problems, we formulate a feasibility maximization problem aiming to maximize the feasibility of the primary and alternative missions. The feasibility maximization problem is based on multi-objective MPC, where each objective (cost function) is associated with a different mission and balanced by a weight vector. Furthermore, the feasibility maximization problem incorporates additional control input horizons toward the alternative missions on top of the control input horizon toward the primary mission, denoted as multihorizon inputs, to evaluate the cost for each mission. We develop the backup plan constrained MPC algorithm, which designs the weight vector that ensures asymptotic stability of the closed-loop system, and generates the optimal control input by solving the feasibility maximization problem with computational efficiency. The performance of the proposed algorithm is validated through simulations of an unmanned aerial vehicle path planning problem.

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots.

With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles. We position our research within the broader context of robotics, emphasizing the fusion of information and decision-making capabilities. The introduction of attention mechanisms enables vehicles to perceive the environment more accurately by focusing on important information and making informed decisions in complex scenarios. By inputting multimodal information, such as images and LiDAR data, into the attention mechanism module, the system can automatically learn and weigh crucial environmental features, thereby placing greater emphasis on key information during obstacle avoidance decisions. Additionally, we leverage the end-to-end architecture and draw from classical theories and algorithms in the field of robotics to enhance the perception and decision-making abilities of autonomous driving vehicles. Furthermore, we address the optimization of path planning using attention mechanisms. We transform the vehicle's navigation task into a sequential decision-making problem and employ LSTM (Long Short-Term Memory) models to handle dynamic navigation in varying environments. By applying attention mechanisms to weigh key points along the navigation path, the vehicle can flexibly select the optimal route and dynamically adjust it based on real-time conditions. Finally, we conducted extensive experimental evaluations and software experiments on the proposed end-to-end architecture on real road datasets. The method effectively avoids obstacles, adheres to traffic rules, and achieves stable, safe, and efficient autonomous driving in diverse road scenarios. This research provides an effective solution for optimizing obstacle avoidance and path planning in the field of autonomous driving. Moreover, it contributes to the advancement and practical applications of multimodal information fusion in navigation, localization, and human-robot interaction.

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

PurposeThe goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.Design/methodology/approachThe path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.FindingsThe algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.Originality/valueAn improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

MPC-PF: Socially and Spatially Aware Object Trajectory Prediction for Autonomous Driving Systems Using Potential Fields

Predicting object motion behaviour is a challenging but crucial task for safe decision making and path planning for autonomous vehicles. It is challenging in large part due to the uncertain, multi-modal, and practically intractable set of possible agent-agent and agent-space interactions, especially in urban driving settings. Models solely based on constant velocity or social force have an inherent bias and may lead to inaccurate predictions across the prediction horizon whereas purely data driven approaches suffer from a lack of holistic set of rules governing predictions. We tackle this problem by introducing MPC-PF: a novel potential field-based trajectory predictor that incorporates social interaction via agent-agent and agent-space considerations and is able to tradeoff between inherent model biases across the prediction horizon. Through evaluation on the Waymo Open Motion Dataset and a variety of other common urban driving scenarios, we show that our model is capable of achieving state-of-the-art performance while producing accurate predictions for both short and long term timesteps. We also demonstrate the significance of our model architecture through an ablation study.

Research on path planning for autonomous vehicle based on Frenet system

Autonomous vehicles drive according to the expected trajectory to achieve horizontal and longitudinal control of the vehicle. Therefore, the research on path planning is of great significance. This paper mainly analyzes the road behavior of the car, studies the five-fold polynomial algorithm model, simulates the path trajectory of different behavior, and analyzes the rate of change of lateral and vertical velocity and lateral and vertical acceleration under different behaviors, to get the car prediction time. Using the optimal control theory, the linear space expression is established to study the variation of transverse velocity, transverse velocity and transverse deflection angle, and the expression of the system to achieve the steady-state response is given. The simulation results are compared and analyzed to find out the optimal path of different scenes and different driving behaviors, which provides theoretical basis for real vehicle debugging.

An accurate and efficient approach to probabilistic conflict prediction

Conflict prediction is a vital component of path planning for autonomous vehicles. Prediction methods must be accurate for reliable navigation, but also computationally efficient to enable online path planning. Efficient prediction methods are especially crucial when testing large sets of candidate trajectories. We present a prediction method that has the same accuracy as existing methods, but up to an order of magnitude faster. This is achieved by rewriting the conflict prediction problem in terms of the first-passage time distribution using a dimension-reduction transform. First-passage time distributions are analytically derived for a subset of Gaussian processes describing vehicle motion. The proposed method is applicable to 2-D stochastic processes where the mean can be approximated by line segments, and the conflict boundary can be approximated by piece-wise straight lines. The proposed method was tested in simulation and compared to two probability flow methods, as well as a recent instantaneous conflict probability method. The results demonstrate a significant decrease of computation time.

TriPField: A 3D Potential Field Model and Its Applications to Local Path Planning of Autonomous Vehicles

Potential fields have been integrated with local path-planning algorithms for autonomous vehicles (AVs) to tackle challenging scenarios with dense and dynamic obstacles. Most existing potential fields are isotropic without considering the traffic agent’s geometric shape and could cause failures due to local minima. We propose a three-dimensional potential field (TriPField) model to overcome this drawback by integrating an ellipsoid potential field with a Gaussian velocity field (GVF). Specifically, we model the surrounding vehicles as ellipsoids in corresponding ellipsoidal coordinates, where the formulated Laplace equation is solved with boundary conditions. Meanwhile, we develop a nonparametric GVF to capture the multi-vehicle interactions and then plan the AV’s velocity profiles, reducing the path search space and improving computing efficiency. Finally, a local path-planning framework with our TriPField is developed by integrating model predictive control to consider the constraints of vehicle kinematics. Our proposed approach is verified in three typical scenarios, i.e., active lane change, on-ramp merging, and car following. Experimental results show that our TriPField-based planner obtains a shorter, smoother local path with a slight jerk during control, especially in the scenarios with dense traffic flow, compared with traditional potential field-based planners. Our proposed TriPField-based planner can perform emergent obstacle avoidance for AVs with a high success rate even when the surrounding vehicles behave abnormally.

Obstacle-Avoidance Path-Planning Algorithm for Autonomous Vehicles Based on B-Spline Algorithm

To solve the problem of the real-time path-planning of autonomous vehicles for obstacle avoidance on structured roads, a path-planning approach based on the B-spline algorithm is proposed in this paper. Firstly, the mechanism of driver path planning is analyzed, and a dynamic risk-identification model based on the support vector machine is proposed. It combines the driver’s risk perception characteristics and a risk model. Then, the B-spline algorithm model is improved based on the risk-identification model. Furthermore, road features, road constraints and dynamic constraints are considered to further optimize the planning algorithm. To verify the path-planning approach proposed in this paper, a co-simulation experiment based on CarSim/Simulink is conducted. Results show that the improved algorithm is effective in static and dynamic obstacles avoidance. The algorithm can generate collision-free obstacle avoidance paths, and the paths meet the real-time requirements and dynamic constraints of obstacle avoidance scenarios. In addition, the proposed algorithm optimizes the path according to the driver’s operating characteristics, which can further improve the safety and comfort of autonomous vehicles.

Local Path Planning of Autonomous Vehicle Based on an Improved Heuristic Bi-RRT Algorithm in Dynamic Obstacle Avoidance Environment.

The existing variants of the rapidly exploring random tree (RRT) cannot be effectively applied in local path planning of the autonomous vehicle and solve the coherence problem of paths between the front and back frames. Thus, an improved heuristic Bi-RRT algorithm is proposed, which is suitable for obstacle avoidance of the vehicle in an unknown dynamic environment. The vehicle constraint considering the driver’s driving habit and the obstacle-free direct connection mode of two random trees are introduced. Multi-sampling biased towards the target state reduces invalid searches, and parent node selection with the comprehensive measurement index accelerates the algorithm’s execution while making the initial path gentle. The adaptive greedy step size, introducing the target direction, expands the node more effectively. Moreover, path reorganization minimizes redundant path points and makes the path’s curvature continuous, and path coherence makes paths between the frames connect smoothly. Simulation analysis clarifies the efficient performance of the proposed algorithm, which can generate the smoothest path within the shortest time compared with the other four algorithms. Furthermore, the experiments on dynamic environments further show that the proposed algorithm can generate a differentiable coherence path, ensuring the ride comfort and stability of the vehicle.

Local Path Planning of the Autonomous Vehicle Based on Adaptive Improved RRT Algorithm in Certain Lane Environments

Given that the rapidly exploring random tree algorithm (RRT) and its variants cannot efficiently solve problems of path planning of autonomous vehicles, this paper proposes a new, adaptive improved RRT algorithm. Firstly, an adaptive directional sampling strategy is introduced to avoid excessive search by reducing the randomness of sampling points. Secondly, a reasonable node selection strategy is used to improve the smoothness of the path by utilizing a comprehensive criterion that combines angle and distance. Thirdly, an adaptive node expansion strategy is utilized to avoid invalid expansion and make the generated path more reasonable. Finally, the expanded ellipse is used to realize vehicle obstacle avoidance in advance, and the post-processing strategy removes redundant line segments of the initial path to improve its quality. The simulation results show that the quality of the planned path is significantly improved. This path followed successfully has good trajectory stability, which shows the proposed algorithm’s effectiveness and practicability in autonomous vehicles’ local path planning.

Fast Path Planning of Autonomous Vehicles in 3D Environments

Three dimensional path planner is crucial for the safe navigation of autonomous vehicles (AV), such as unmanned aerial vehicles or unmanned underwater vehicles, which operate in three dimensions. In this paper, we develop a novel 3D path planner, which is fast in generating a near-optimal solution path. The planner generates the 3D path considering the size of an AV so that as the AV traverses the constructed path, it does not collide with an obstacle. This paper introduces a 3D path planner with novel concepts, such as a virtual agent and virtual sensors. In order to generate a 3D path to the goal as fast as possible, we let the virtual agent deploy virtual sensors iteratively, such that the connected sensor network can be formed. The constructed sensor network serves as a topological map for the AV, and we find a shortest path from the start to the goal utilizing the network. The virtual agent’s maneuver is biased towards the goal, in order to find a path to the goal as fast as possible. Moreover, the size of the agent is set considering the safety margin of the generated path. Through MATLAB simulations, we demonstrate the outperformance (low computational load and short path length) of our 3D path planner by comparing it with the 3D RRT-star algorithm.

Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion

Local path planning is a key task for the motion planners of autonomous vehicles since it commands the vehicle across its environment while avoiding any obstacles. To perform this task, the local path planner generates a trajectory and a velocity profile, which are then sent to the vehicle’s actuators. This paper proposes a new local path planner for autonomous vehicles based on the Attractor Dynamic Approach (ADA), which was inspired by the behavior of movement of living beings, along with an algorithm that takes into account four acceleration policies, the ST dynamic vehicle model, and several constraints regarding the comfort and security. The original functions that define the ADA were modified in order to adapt it to the non-holonomic vehicle’s constraints and to improve its response when an impact scenario is detected. The present approach is validated in a well-known simulator for autonomous vehicles under three representative cases of study where the vehicle was capable of generating local paths that ensure the security of the vehicle in such cases. The results show that the approach proposed in this paper is a promising tool for the local path planning of autonomous vehicles since it is able to generate trajectories that are both safe and efficient.

Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

Different driving styles should be considered in path planning for autonomous vehicles that are travelling alongside other traditional vehicles in the same traffic scene. Based on the drivers’ characteristics and artificial potential field (APF), an improved local path planning algorithm is proposed in this paper. A large amount of driver data are collected through tests and classified by the K-means algorithm. A Keras neural network model is trained by using the above data. APF is combined with driver characteristic identification. The distances between the vehicle and obstacle are normalized. The repulsive potential field functions are designed according to different driver characteristics and road boundaries. The designed local path planning method can adapt to different surrounding manual driving vehicles. The proposed human-like decision path planning method is compared with the traditional APF planning method. Simulation tests of an individual driver and various drivers with different characteristics in overtaking scenes are carried out. The simulation results show that the curves of human-like decision-making path planning method are more reasonable than those of the traditional APF path planning method; the proposed method can carry out more effective path planning for autonomous vehicles according to the different driving styles of surrounding manual vehicles.

Review on path planning algorithm for unmanned aerial vehicles

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.

A bat-pigeon algorithm to crack detection-enabled autonomous vehicle navigation and mapping

• An image processing algorithm is developed to accurately locate the cracks with crack detection. • The proposed Bat-Pigeon algorithm effectively navigates the autonomous vehicle to decelerate around small crack areas while planning a collision-free path with the least travel time. • A local reactive navigator is developed that builds environmental maps locally while avoiding moving obstacles. • Simulation and comparative studies validate that the proposed algorithms effectively carry out the adjustable speed navigation and mapping of autonomous vehicles based on road conditions in various real-world scenarios. Autonomous vehicles have nowadays received widespread attention, and path planning is one of the most important components of its autonomous operation. However, due to the long-term use of roads and lack of maintenance, the roads that autonomous vehicles need to pass inevitably have cracks. In this case, while the autonomous vehicles pass through these cracked areas at high speed, it will increase the sense of bumps and even deviate from the originally planned route, which may potentially cause vehicle damage. Therefore, in this paper, we propose an adjustable speed navigation method in light of crack detection for autonomous vehicle path planning, which can automatically adjust the speed in the cracked areas of the road. Based on the obtained image of the road environment, an image processing algorithm to accurately locate the crack is developed. Then, in light of the obtained location of cracks and obstacles, a Bat-Pigeon algorithm (BPA) is proposed to conduct adjustable speed navigation of autonomous vehicles. Take advantage of the same individual speed update rule, we integrate the global search of the Pigeon-inspired optimization (PIO) algorithm and the local search by the Bat algorithm (BA), which can effectively improve the speed and performance of the convergence algorithm. The proposed Bat-Pigeon algorithm navigates the autonomous vehicle to decelerate around small crack areas while planning a collision-free path with the least travel time. In addition, a local reactive navigator is developed that builds environmental maps locally while avoiding dynamic and unknown obstacles. To verify the theoretical advantages of the developed algorithms, we perform comparative experiments under various scenarios. Simulation and comparative studies validate that the proposed algorithms effectively carry out the adjustable speed navigation and mapping of autonomous vehicles based on road conditions in various real-world scenarios.

Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

Intersections are quite important and complex traffic scenarios, where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles. Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined, this paper proposes a long short-term memory based (LSTM-based) framework that combines intention prediction and trajectory prediction together. First, we build an intersection prior trajectories model (IPTM) by clustering and statistically analyzing a large number of prior traffic flow trajectories. The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory, and also serves as a reference for credibility evaluation. Second, we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage. Furthermore, the predicted intention is also a key that is associated with the prior trajectories model. The proposed framework is validated on two publically released datasets, next generation simulation (NGSIM) and INTERACTION. Compared with other prediction methods, our framework is able to sample a trajectory from the estimated distribution, with its accuracy improved by about 20%. Finally, the credibility evaluation, which is based on the prior trajectories model, makes the framework more practical in the real-world applications.

An Intelligent Lane-Changing Behavior Prediction and Decision-Making Strategy for an Autonomous Vehicle

In the future complex intelligent transportation environments, lane-changing behavior of surrounding vehicles is a significant factor affecting the driving safety. It is necessary to predict the lane-changing behaviors accurately. The driving environments and drivers are the main factors of lane-changing. To comprehensively consider their relationship, this article proposes a prediction method based on a fuzzy inference system (FIS) and a long short-term memory (LSTM) neural network. First, to highly integrate driving environments with drivers, drivers’ cognitive processes of driving environments are simulated using FIS. Fuzzy rules are formulated based on drivers’ cognition, and then driving environments information can be transformed into lane-changing feasibility. Second, the obtained lane-changing feasibility and corresponding vehicle trajectory are designed as input variables of LSTM neural network to predict the lane-changing behavior. Third, based on the above prediction results, an intelligent decision-making strategy is designed for path planning of autonomous vehicle to ensure driving safety. The prediction method is trained and tested by the next generation simulation (NGSIM) dataset, which is made up of real vehicle trajectories. The accurate rate of the method is 92.40%. Moreover, the decision strategy is simulated and verified in hardware-in-the-loop system. Results show that the strategy can significantly improve the performance of driving in dealing with lane-changing behaviors.

Research on Path Planning for Autonomous Vehicles Based on Acceleration for Collision Avoidance

Research on Path Planning for Autonomous Vehicles Based on Acceleration for Collision Avoidance

A novel IPSO technique for path navigation and obstacle avoidance

Path navigation using meta-heuristic optimisation is a popular research topic in the field of autonomous vehicle path planning. In this paper, an improved particle swarm optimisation (IPSO) technique has been presented for unmanned vehicle path navigation which is the enhanced version of existing PSO technique. IPSO is used to solve the convergence speed problem which is a general practice in optimisation algorithms. The modified fitness function of the proposed algorithm is designed in such a manner that causes to converge the algorithm fast. IPSO technique is dependent upon the working of modified fitness function for the path optimisation in an unknown environment. After several iterations, the best cost is computed which is utilised to find the optimised path in mobile robot path navigation. The simulation results clearly indicate that IPSO is competent enough when compared with the existing optimisation methods (genetic algorithm, invasive weed optimisation (IWO)).