Path Planning Strategy for Vehicle Navigation Based on User Habits

Chen, Pengzhan; Zhang, Xiaoyan; Chen, Xiaoyue; Liu, Mengchao

doi:10.3390/app8030407

Cited by 13 publications

(11 citation statements)

References 39 publications

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“…Static and dynamic safety, comfortability, appropriate acceleration and speed for the vehicle [8] Personalized path planner with fuzzy c-means clustering Simulated grids, road simulation model in Changsha Improved personalization of existing path planning [9] Improved rapidly exploring random tree (RRT) algorithm…”

Section: Ref #mentioning

confidence: 99%

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A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

et al. 2021

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Path planning can be perceived as a combination of searching and executing the optimal path between the start and destination locations. Deliberative planning capabilities are essential for the motion of autonomous unmanned vehicles in real-world scenarios. There is a challenge in handling the uncertainty concerning the obstacles in a dynamic scenario, thus requiring an intelligent, robust algorithm, with the minimum computational overhead. In this work, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time. The ant colony system’s pheromone update mechanism was enhanced with a sigmoid gain function for effective exploitation during path planning. Collision avoidance was achieved through the proposed fuzzy logic control. The results were validated using occupancy grids of variable size, and the results were compared against existing methods concerning performance metrics, namely, time and length. The consistency of the algorithm was also analyzed, and the results were statistically verified.

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Section: Ref #mentioning

confidence: 99%

“…Thus the new quantity added will enable quicker pathfinding by eliminating unwanted traversals during the local search. Pheromone gain is given by (8).…”

Section: Calculating Gainmentioning

confidence: 99%

A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

et al. 2021

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“…In recent years, some navigation methods have been proposed for the navigation in urban road networks [6], [11], [15], [26], [37]. Some researches focus on the fastest path, [28] proposed a method to select the fastest road, records the travel time of distribution vehicles on each road, replaces the distance value with the travel time value, and uses the improved Floyd algorithm to calculate the fastest route to complete the distribution task by taking travel time, weather condition, intersection number and other factors into consideration, [16] improved the faster criterion in vehicle routing by extending the bi-delta distribution to the binormal distribution, [19] implemented comfort-based route planning.…”

Section: The Navigation Systems For the Path Planningmentioning

confidence: 99%

A Novel Vector-Based Dynamic Path Planning Method in Urban Road Network

Cai

Cui

et al. 2020

IEEE Access

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The optimal path planning is one of the hot spots in the research of intelligence transportation and geographic information systems. There are many productions and applications in path planning and navigation, however due to the complexity of urban road networks, the difficulty of the traffic prediction increases. The optimal path means not only the shortest distance in geography, but also the shortest time, the lowest cost, the maximum road capacity, etc. In fast-paced modern cities, people tend to reach the destination with the shortest time. The corresponding paths are considered as the optimal paths. However, due to the high data sensing speed of GPS devices, it is different to collect or describe real traffic flows. To address this problem, we propose an innovative path planning method in this paper. Specially, we first introduce a crossroad link analysis algorithm to calculate the real-time traffic conditions of crossroads (i.e. the CrossRank values). Then, we adopt a CrossRank value based A-Star for the path planning by considering the real-time traffic conditions. To avoid the high volume update of CrossRank values, a R-Tree structure is proposed to dynamically update local CrossRank values from the multi-level subareas. In the optimization process, to achieve desired navigation results, we establish the traffic congestion coefficient to reflect different traffic congestion conditions. To verify the effectiveness of the proposed method, we use the actual traffic data of Beijing. The experimental results show that our method is able to generate the appropriate path plan in the peak and low dynamic traffic conditions as compared to online applications. INDEX TERMS CrossRank, path planning, vector, heuristic algorithm.

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“…The architecture offers a probabilistic model that allows us to predict the user's next actions and to identify anomalous user behaviors. The third article proposes a personalized path decision algorithm that is based on user habits for path planning of self-driving travel [16]. Results show that the algorithm can meet the personalized requirements of the user path selection in the path decision.…”

Section: The Papersmentioning

confidence: 99%