Vehicle Path Research Articles

Oceanic Challenges to Technological Solutions: A Review of Autonomous Underwater Vehicle Path Technologies in Biomimicry, Control, Navigation, and Sensing

Oceanic Challenges to Technological Solutions: A Review of Autonomous Underwater Vehicle Path Technologies in Biomimicry, Control, Navigation, and Sensing

A Mixed Generative Adversarial Imitation Learning Based Vehicle Path Planning Algorithm

A Mixed Generative Adversarial Imitation Learning Based Vehicle Path Planning Algorithm

Simulation Research on Logistics Distribution Path Based on Colony Intelligence Optimisation

Abstract In light of the growing emphasis on “logistics integration,” this paper contends that the logistics network system requires comprehensive integration and optimization. We propose the use of the Bacterial Foraging Optimization (BFO) algorithm to enhance the Vehicle Routing Problem (VRP). The penalty function method is employed to direct the BFO algorithm in identifying the optimal vehicle path encoding and to improve the convergence operations for logistics distribution. This study analyzes the fundamental characteristic elements of the VRP, introduces a basic model for the VRP, and develops a logistics distribution path optimization model that minimizes the total cost by thoroughly considering the expenses associated with cold chain low-carbon logistics distribution. A simulation environment is established, and algorithm parameters are set to determine the optimal solutions for both the BFO and the Improved BFO (IBFO) algorithms in the test function. An example is selected for practical application, wherein the design of the distribution center and points is carried out, and the colony intelligence optimization algorithm is applied to simulate the logistics and distribution paths. Combined with the specific case of cold chain distribution of food, the optimization of the cold chain logistics distribution path is solved. In the case study, when the carbon penalty coefficient is ε = 15, the optimal logistics and distribution site selection program is Guangxi and Guizhou, and the total cost of the supply chain is 16,363,365.92 yuan. At this time, the supply chain cost is optimal. Compared with the carbon penalty coefficient ε = 20, the economic cost increased by 70152.24 yuan, but the carbon emission decreased by 1890.9 kg. The optimization of logistics and distribution processes encourages enterprises to develop strategies that promote low carbon and environmental protection and achieve better economic and ecological outcomes.

Research on vehicle routing problem of automobile part Milk-Run with time windows constrained

Based on the parts inbound logistics as the research object, this thesis introduces Milk-run of third party logistics enterprise mode. Establish mathematical model under the premise of time window, through genetic algorithm optimize the distribution route, and using MATLAB to simulation, the results show that distribution vehicles reduced from 18 to 6. Transportation costs were reduced by 30.05%. Though the research on the circular pick-up mode of incoming logistics of parts and components, this thesis explores the strategy of optimizing its transportation path. On the one hand, it can solve the contradiction of reducing inventory cost but increasing transportation cost to a certain extent, and effectively reduce the cost of the nodes of the whole supply chain, which has certain theoretical value and significance for the research of vehicle path optimization; On the other hand, a reasonable distribution route can reduce driving distance and alleviate urban traffic pressure, while reducing distribution vehicles can reduce urban noise and automobile exhaust emissions, and contribute to the realization of low-carbon environmental protection.

Fixed-Time Approach for Automated Ground Vehicles Path Following Subject to Prescribed Error Constraints and Completely Unknown Steering Dead Zone

Fixed-Time Approach for Automated Ground Vehicles Path Following Subject to Prescribed Error Constraints and Completely Unknown Steering Dead Zone

Vehicle Path Planning Using Variable Neighborhood A* Algorithm Based on Driving Risk Field

Vehicle Path Planning Using Variable Neighborhood A* Algorithm Based on Driving Risk Field

A Fast Predictive Control Method for Vehicle Path Tracking Based on a Recurrent Neural Network

A Fast Predictive Control Method for Vehicle Path Tracking Based on a Recurrent Neural Network

Improved Q-Learning Algorithm Based on Flower Pollination Algorithm and Tabulation Method for Unmanned Aerial Vehicle Path Planning

Improved Q-Learning Algorithm Based on Flower Pollination Algorithm and Tabulation Method for Unmanned Aerial Vehicle Path Planning

Joint Vehicle Path Planning for Interruptible 1-D Snake-like Platoons and 2D Flocks in Lane-free Traffic

This article presents a joint trajectory optimization algorithm for connected and automated vehicles forming 1-D snake-like interruptible platoons or 2-D deformable and interruptible flocks in a lane-free traffic environment on highways. A double double-integrator model is employed for the longitudinal and lateral movements of each vehicle. An appropriately defined Euclidian distance to track the front vehicle is utilized to establish and operate a 1-D platoon of vehicles, at selectable distance from each other. The Euclidian distance is used as a soft constraint, which allows for platoons to divide, if required to avoid obstacles, and reunite eventually. Moreover, the distance from a selectable frame (e.g. a triangle) are employed as soft constraints to form and maintain a 2-D deformable and interruptible flock of vehicles. The flock may feature a leader vehicle, e.g. at the front corner of a triangle. A collision avoidance function considering constant time-gaps or space-gaps, is used based on the elliptical distance between each two vehicles or external obstacles. A joint multi-objective function is formulated and minimized using an efficient feasible direction algorithm with respect to constant and state-dependent bounds on control inputs, including road boundary constraints. This leads to creating a flexible platoon or deformable flock accounting for low fuel consumption, passenger convenience, collision avoidance, achievement of desired speed and prevention of infeasible maneuvers. Challenging scenarios are examined on a lane-free straight motorway stretch, producing promising results for further exploration of the proposed concepts within an MPC (model predictive control) framework.

Study on the Development History and Expansion Path of Electric Vehicles in Henan Province

Study on the Development History and Expansion Path of Electric Vehicles in Henan Province

Equilibrium optimizer with generalized opposition-based learning for multiple unmanned aerial vehicle path planning

Equilibrium optimizer with generalized opposition-based learning for multiple unmanned aerial vehicle path planning

Modeling Vehicle Paths at Intersections: A Unified Approach Based on Entrance and Exit Lanes

Modeling Vehicle Paths at Intersections: A Unified Approach Based on Entrance and Exit Lanes

A Novel Triangular-Based Estimation Technique for Bezier Curve Control Points Generation on Autonomous Vehicle Path Planning at the Roundabout Intersection

A Novel Triangular-Based Estimation Technique for Bezier Curve Control Points Generation on Autonomous Vehicle Path Planning at the Roundabout Intersection

Subjective risk and associated electrodermal activity of a self-driving car passenger in an urban shared space.

Shared spaces are urban areas without physical separation between motorised and non-motorised users. Previous research has suggested that it is difficult for users to appropriate these spaces and that the advent of self-driving cars could further complicate interactions. It is therefore important to study the perception of these spaces from the users' perspectives to determine which conditions may promote their acceptance of the vehicles. This study investigates the perceived collision risk of a self-driving car's passenger when pedestrians cross the vehicle's path. The experiment was conducted with a driving simulator. Seven factors were manipulated to vary the dynamics of the crossing situations in order to analyse their influence on the passenger's perception of collision risk. Two measures of perceived risk were obtained. A continuous subjective assessment, reflecting an explicit risk evaluation, was reported in real time by participants. On the other hand, their skin conductance responses, which reflects implicit information processing, were recorded. The relationship between the factors and the risk perception indicators was studied using Bayesian networks. The best Bayesian networks demonstrate that subjective collision risk assessments are primarily influenced by the factors that determine the relative positions of the vehicle and the pedestrian as well as the distance between them when they are in close proximity. The analysis further reveals that variations in skin conductance response indicators are more likely to be explained by variations in subjective assessments than by variations in the manipulated factors. These findings could benefit the development of self-driving navigation among pedestrians by improving understanding of the factors that influence passengers' feelings.

False Alarm Effects in Early Warnings for Emergency Vehicles: Exploring Drivers' Move-Over Behavior.

This study investigated drivers' move-over behavior when receiving an Emergency Vehicle Approaching (EVA) warning. Furthermore, the possible effects of false alarms, driver experience, and modality on move-over behavior were explored. EVA warnings are one solution to encourage drivers to move over for emergency vehicles in a safe and timely manner. EVA warnings are distributed based on the predicted path of the emergency vehicle causing a risk of false alarms. Previous EVA studies have suggested a difference between inexperienced and experienced drivers' move-over behavior. A driving simulator study was conducted with 110 participants, whereof 54 inexperienced and 56 experienced drivers. They were approached by an emergency vehicle three times. A control group received no EVA warnings, whereas the experimental groups received either true or false warnings, auditory or visual, 15seconds before the emergency vehicle overtook them. Drivers who received EVA warnings moved over more quickly for the emergency vehicle compared to the control group. Drivers moved over more quickly for each emergency vehicle interaction. False alarms impaired move-over behavior. No difference in driver behavior based on driver experience or modality was observed. EVA warnings positively affect drivers' move-over behavior. However, false alarms can decrease drivers' future willingness to comply with the warning. The findings regarding measurements of delay can be used to optimize the design of future EVA systems. Moreover, this research should be used to further understand the effect of false alarms in in-car warnings.

Smooth Sliding Mode Control for Path Following of Underactuated Surface Vehicles Based on LOS Guidance

In this paper, a solution to the problem of following a curved path for underactuated unmanned surface vehicles (USVs) with unknown sideslip angle and model uncertainties is studied. A novel smooth sliding mode control (SSMC) based on a finite-time extended state observer (FTESO) for heading control is proposed. Firstly, the model of a USV with rudderless double thrusters is established. Secondly, the path-following error dynamics of a USV is established in a path-tangential reference frame. Thirdly, a finite-time observer is introduced to estimate the unidentified sideslip angle, and the line-of-sight (LOS) guidance law is applied to produce the desired heading angle. Finally, an SSMC controller is proposed to force USV tracking at the desired heading angle and surge speed, in which FTESO is used to estimate and compensate the unknown disturbance in sliding mode dynamics. The theoretical analysis for FTESO-SSMC verifies that the controller can provide finite-time convergence to and stability on the sliding surface. Simulation studies and contrast test are conducted to demonstrate the robustness and rapidity of the proposed FTESO-SSMC controller.

Optimization of Green Vehicle Paths Considering the Impact of Carbon Emissions: A Case Study of Municipal Solid Waste Collection and Transportation

In recent years, the waste produced as a result of the production and consumption activities of urban residents has led to significant environmental degradation and resource wastage. This paper focuses on the research object of municipal solid waste (MSW) collection and transportation based on the concept of “sustainable development and green economy”. Firstly, this study examines the current state of urban domestic garbage collection and transportation. It analyzes the following challenges and deficiencies of the existing collection and transportation system: (1) the operating efficiency of garbage collection vehicles is low, resulting in a significant accumulation of waste on the roadside and within the community; (2) the vehicle collection and transportation routes are fixed, and there are empty vehicles running; (3) the amount of garbage on a route exceeds the vehicle’s loading capacity, which requires the vehicle to perform a second round of collection and transportation. To enhance the efficiency of urban garbage collection and transportation and minimize the collection and transportation costs, we are investigating the problem of optimizing the path for green vehicles. To comprehensively optimize the fixed cost, variable cost, and carbon emission cost incurred during vehicle operation, a vehicle routing model with time windows is established, taking into account vehicle load constraints. Carbon emission coefficient and carbon tax parameters are introduced into the model and the “fuel-carbon emission” conversion method is used to measure the carbon cost of enterprises. An improved ant colony optimization (ACO) method is proposed: (1) the introduction of a vehicle load factor improves the ant state transfer method; (2) the updated pheromone method is improved, and additional pheromone is added to both the feasible path and the path with the minimum objective function; (3) the max–min ACO algorithm is introduced to address the issue of premature convergence of the algorithm; (4) the embedding of a 2-opt algorithm further prevents the ACO algorithm from falling into the local optimum. Finally, the calculation results based on the example data demonstrate that the algorithm has a significant advantage over the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The total transportation distance determined by this algorithm is shorter than that of the GA and PSO methods, and the total cost of the scheme is 1.66% and 1.89% lower than that determined by GA and PSO, respectively. Compared to the data from the actual case, the number of vehicles required in the operation of this algorithm and model is reduced by three. Additionally, the total cost, fixed cost, and carbon emission cost incurred by the vehicles during operation were reduced by 31.2%, 60%, and 25.3% respectively. The results of this study help the station to collect and distribute waste efficiently, while also achieving the goals of energy saving, consumption reduction, and emission reduction.

A Dynamic Path Optimization Model of IOT Delivery Vehicles for E-commerce Logistics Distribution

Logistics and distribution is a vital link to guarantee the stable supply of the e-commerce market and the healthy development of the industry. With the constant growth of the e-commerce, the efficiency and service quality of logistics and distribution have been paid more and more attention to. Therefore, the study firstly Considering distribution fixed cost, transportation penalty cost and carbon emission cost, the vehicle routing optimization model is transformed into the lowest transportation cost model, then uses an improved traditional artificial fish swarm algorithm to find the optimum way for this model, and finally verifies its performance and applicability through experiments. The performance test results show that the algorithm finds the optimal solution 3589 and 3590 in 63 and 78 iterations in the Oxford Robot Car dataset and Apollo Scape dataset, respectively; the average running time of the algorithm is 11.864s and 11.967s in the 10 operation time tests; in the operation function test, the algorithm. The algorithm was able to overcome the local optimal solution problem. The applicability simulation shows that this algorithm stabilizes after 53 iterations, the minimum cost of the optimal solution of the model is $41,224, and the total distance of distribution is 9035 km. The research algorithm is fast in finding the optimal value, which is close to it, indicating that the algorithm is highly efficient and reliable, and can greatly optimize the path of e-commerce logistics delivery vehicles, and give a theoretical foundation for the optimization of logistics delivery paths in other industries.

Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading

As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution.

Distributed edge signal control for cooperating pre-planned connected automated vehicle path and signal timing at edge computing-enabled intersections

The emerging technologies in both wireless communication and automation driving that have enabled vehicles to communicate with infrastructure, and with each other, are collectively known as connected vehicle and automated vehicle (CAV) technology. In existing connected automated vehicle-based and traditional traffic control systems, urban arterials with signalized intersections still face issues, including local demand uncertainty and automation level compatibility. Here, a distributed signal control system that integrates automated vehicle path planning information is adapted to address these issues. The proposed method includes planning-based local demand estimation and demand-aware max pressure control in an edge computing environment. A bivariable automation model is developed to represent the various, i.e., L1-L5, automatic driving levels. A real district with typical scenarios is implemented to verify the proposed method. The simulation results show that the proposed demand-aware edge control provides better control results than benchmarks for almost all different performance indices and network demands. The proposed control reduces the waiting time by up to 30%, and the time loss decreased by up to 20%. Moreover, better performances are always obtained at different automation levels, so there is a good compatibility of the automation driving for the adaptation of the CAV flows, especially at the L3-L5 levels.