Research on Path Planning Algorithm for Driverless Vehicles

Ma, Hao; Pei, Wenhui; Zhang, Qi

doi:10.3390/math10152555

Cited by 10 publications

(5 citation statements)

References 53 publications

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“…Moreover, the radar also measures the object's velocity ( . ρ), as given by Equation (14). Therefore, to unify the way of measurement, a mapping function is used (in Equation ( 15)) to convert the lidar Cartesian co-ordinates to polar form.…”

Section: Ukf-based Lidar/radar Fusionmentioning

confidence: 99%

“…Additionally, the path-planning task is concerned with how an autonomous car drives from one point on the map (the initial position for the trip) to the final goal (the final position for the trip) [14]. It is divided into two subtasks: global path planning, which is the high-level path on the map for the desired trip, and local path planning, which generates a trajectory profile and a velocity profile for the egocar to maneuver across its environment while avoiding any obstacles, changing lanes, passing other vehicles, etc.…”

Section: Introductionmentioning

confidence: 99%

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Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization

Farag,

Barakat

2023

WEVJ

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An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and implemented. In dealing with the obtained sensory data or given knowledge about the vehicle’s surroundings, the proposed method takes a probabilistic approach. In this approach, the involved probability densities are expressed by keeping a collection of samples selected at random from them (Monte Carlo simulation). Consequently, this Monte Carlo sampling allows the resultant position estimates to be represented with any arbitrary distribution, not only a Gaussian one. The selected technique to implement this Monte-Carlo-based localization is Bayesian filtering with particle-based density representations (i.e., particle filters). The employed particle filter receives the surrounding object ranges from a carefully tuned Unscented Kalman Filter (UKF) that is used to fuse radar and lidar sensory readings. The sensory readings are used to detect pole-like static objects in the egocar’s surroundings and compare them to the ones that exist in a supplied detailed reference map that contains pole-like landmarks that are produced offline and extracted from a 3D lidar scan. Comprehensive simulation tests were conducted to evaluate the outcome of the proposed technique in both lateral and longitudinal localization. The results show that the proposed technique outperforms the other techniques in terms of smaller lateral and longitudinal mean position errors.

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Section: Ukf-based Lidar/radar Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization

Farag,

Barakat

2023

WEVJ

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“…W Wei et al studied the longdistance autonomous path planning of intelligent vehicles by using the Dijkstra algorithm and studied the autonomous obstacle avoidance of intelligent vehicles by using the artificial potential field method combined with the direction sensor and infrared sensor [50]. Hao Ge, Xin Li, Leiyan Yu et al also studied the path planning method through different methods [51][52][53][54][55]. These methods implement and optimize the path planning from different aspects, but the establishment of the environment map is based on free modeling, and the path planning is not implemented in the real environment.…”

Section: Related Workmentioning

confidence: 99%

Non-Standard Map Robot Path Planning Approach Based on Ant Colony Algorithms

Li,

Kim,

2023

Sensors

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Robot path planning is an important component of ensuring the robots complete work tasks effectively. Nowadays, most maps used for robot path planning obtain relevant coordinate information through sensor measurement, establish a map model based on coordinate information, and then carry out path planning for the robot, which is time-consuming and labor-intensive. To solve this problem, a method of robot path planning based on ant colony algorithms after the standardized design of non-standard map grids such as photos was studied. This method combines the robot grid map modeling with image processing, bringing in calibration objects. By converting non-standard actual environment maps into standard grid maps, this method was made suitable for robot motion path planning on non-standard maps of different types and sizes. After obtaining the planned path and pose, the robot motion path planning map under the non-standard map was obtained by combining the planned path and pose with the non-standard real environment map. The experimental results showed that this method has a high adaptability to robot non-standard map motion planning, can realize robot path planning under non-standard real environment maps, and can make the obtained robot motion path display more intuitive and convenient.

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“…In addition to improving the potential field function, the APF algorithm can also be integrated with other algorithms. This includes the Rapid Exploration Random Tree (RRT) algorithm [11], A* algorithm [12], complex resistivity method [13], ant colony algorithm [14], and cyclic reinforcement learning algorithm [15]. The fusion algorithm can enable autonomous vehicles to avoid obstacles more safely and achieve the purpose of complementary advantages.…”

Section: Introductionmentioning

confidence: 99%

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

Zhai,

Pei

2023

Sustainability

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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.

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Research on Path Planning Algorithm for Driverless Vehicles

Cited by 10 publications

References 53 publications

Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization

Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization

Non-Standard Map Robot Path Planning Approach Based on Ant Colony Algorithms

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

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