Lane change and path planning of autonomous vehicles using GIS

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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Automatic Lane-Changing Decision Based on Single-Step Dynamic Game with Incomplete Information and Collision-Free Path Planning

Actuators ◽

10.3390/act10080173 ◽

2021 ◽

Vol 10 (8) ◽

pp. 173

Author(s):

Hongbo Wang ◽

Shihan Xu ◽

Longze Deng

Keyword(s):

Path Planning ◽

Dynamic Game ◽

Single Step ◽

Bézier Curve ◽

Vehicle Safety ◽

Bezier Curve ◽

Lane Change ◽

Hardware In The Loop ◽

Power Performance ◽

Lane Changing

Traffic accidents are often caused by improper lane changes. Although the safety of lane-changing has attracted extensive attention in the vehicle and traffic fields, there are few studies considering the lateral comfort of vehicle users in lane-changing decision-making. Lane-changing decision-making by single-step dynamic game with incomplete information and path planning based on Bézier curve are proposed in this paper to coordinate vehicle lane-changing performance from safety payoff, velocity payoff, and comfort payoff. First, the lane-changing safety distance which is improved by collecting lane-changing data through simulated driving, and lane-changing time obtained by Bézier curve path planning are introduced into the game payoff, so that the selection of the lane-changing start time considers the vehicle safety, power performance and passenger comfort of the lane-changing process. Second, the lane-changing path without collision to the forward vehicle is obtained through the constrained Bézier curve, and the Bézier curve is further constrained to obtain a smoother lane-changing path. The path tracking sliding mode controller of front wheel angle compensation by radical basis function neural network is designed. Finally, the model in the loop simulation and the hardware in the loop experiment are carried out to verify the advantages of the proposed method. The results of three lane-changing conditions designed in the hardware in the loop experiment show that the vehicle safety, power performance, and passenger comfort of the vehicle controlled by the proposed method are better than that of human drivers in discretionary lane change and mandatory lane change scenarios.

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Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

Applied Sciences ◽

10.3390/app11115057 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5057

Author(s):

Wan-Yu Yu ◽

Xiao-Qiang Huang ◽

Hung-Yi Luo ◽

Von-Wun Soo ◽

Yung-Lung Lee

Keyword(s):

Path Planning ◽

Task Allocation ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Dynamic Task ◽

The Core ◽

Dynamic Task Allocation ◽

Vehicle Technology ◽

Future Work ◽

Complicated Task

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

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A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽

10.3390/s21041523 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1523

Author(s):

Nikita Smirnov ◽

Yuzhou Liu ◽

Aso Validi ◽

Walter Morales-Alvarez ◽

Cristina Olaverri-Monreal

Keyword(s):

Game Theory ◽

Decision Making ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Urban Traffic ◽

Road User ◽

Lane Change ◽

Traffic Light ◽

User Interactions ◽

Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

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