Simple Clothoid Paths for Autonomous Vehicle Lane Changes at the Limits of Handling

2013 ◽  
Author(s):  
Joseph Funke ◽  
J. Christian Gerdes
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Lane Change ◽  
Lane Changes ◽  
Entry Speed ◽  
Path Curvature

This paper demonstrates that an autonomous vehicle can perform emergency lane changes up to the limits of handling through real-time generation and evaluation of bi-elementary paths. Path curvature and friction limits determine the maximum possible speed along the path and, consequently, the feasibility of the path. This approach incorporates both steering inputs and changes in speed during the maneuver. As a result, varying path parameters and observing the maximum possible entry speed of resulting paths gives insight about when and to what extent a vehicle should brake and turn during emergency lane change maneuvers. Tests on an autonomous vehicle validate this approach for lane changes at the limits of handling.

Simple Clothoid Lane Change Trajectories for Automated Vehicles Incorporating Friction Constraints

2015 ◽  
Vol 138 (2) ◽  
Author(s):  
Joseph Funke ◽  
J. Christian Gerdes
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Lane Change ◽  
Automated Vehicles ◽  
Change Trajectories ◽  
Lane Changes ◽  
Entry Speed ◽  
Path Curvature

This paper demonstrates that an autonomous vehicle can perform emergency lane changes up to the friction limits through real-time generation and evaluation of bi-elementary paths. Path curvature and friction determine the maximum possible speed along the path and, consequently, the feasibility of the path. This approach incorporates both steering inputs and changes in speed during the maneuver. As a result, varying path parameters and observing the maximum possible entry speed of resulting paths give insight about when and to what extent a vehicle should brake and turn during emergency lane change maneuvers. Tests on an autonomous vehicle validate this approach for lane changes near the limits of friction.

Design and implementation of human driving data–based active lane change control for autonomous vehicles

2020 ◽  
Vol 235 (1) ◽  
pp. 55-77
Author(s):  
Heungseok Chae ◽  
Yonghwan Jeong ◽  
Hojun Lee ◽  
Jongcherl Park ◽  
Kyongsu Yi
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
High Speed ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Lane Change ◽  
Driving Mode ◽  
Lane Changes ◽  
Safety Indices

This article describes the design, implementation, and evaluation of an active lane change control algorithm for autonomous vehicles with human factor considerations. Lane changes need to be performed considering both driver acceptance and safety with surrounding vehicles. Therefore, autonomous driving systems need to be designed based on an analysis of human driving behavior. In this article, manual driving characteristics are investigated using real-world driving test data. In lane change situations, interactions with surrounding vehicles were mainly investigated. And safety indices were developed with kinematic analysis. A safety indices–based lane change decision and control algorithm has been developed. In order to improve safety, stochastic predictions of both the ego vehicle and surrounding vehicles have been conducted with consideration of sensor noise and model uncertainties. The desired driving mode is decided to cope with all lane changes on highway. To obtain desired reference and constraints, motion planning for lane changes has been designed taking stochastic prediction-based safety indices into account. A stochastic model predictive control with constraints has been adopted to determine vehicle control inputs: the steering angle and the longitudinal acceleration. The proposed active lane change algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable lane changes in high-speed driving on highways have been demonstrated using our autonomous test vehicle.

scholarly journals Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change

2020 ◽  
Vol 10 (5) ◽  
pp. 1626 ◽  
Author(s):  
Xiaodong Wu ◽  
Bangjun Qiao ◽  
Chengrui Su
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicle ◽  
Safety Margin ◽  
Autonomous Driving ◽  
Simulation System ◽  
Lane Change ◽  
Lane Changing ◽  
Lane Changes ◽  
Gray Prediction ◽  
Safety Margins

A lane change is one of the most important driving scenarios for autonomous driving vehicles. This paper proposes a safe and comfort-oriented algorithm for an autonomous vehicle to perform lane changes on a straight and level road. A simplified Gray Prediction Model is designed to estimate the driving status of surrounding vehicles, and time-variant safety margins are employed during the trajectory planning to ensure a safe maneuver. The algorithm is able to adapt its lane changing strategy based on traffic situation and passenger demands, and features condition-triggered rerouting to handle unexpected traffic situations. The concept of dynamic safety margins with different settings of parameters gives a customizable feature for the autonomous lane changing control. The effect of the algorithm is verified within a self-developed traffic simulation system.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

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.

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

Sensors ◽  
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.

The design of an intelligent real‐time autonomous vehicle, TAIWANiTS‐1

2007 ◽  
Vol 30 (5) ◽  
pp. 829-842 ◽  
Author(s):  
Bing‐Fei Wu ◽  
Chao‐Jung Chen ◽  
Hsin‐Han Chiang ◽  
Hsin‐Yuan Peng ◽  
Jau‐Woei Perng ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Time Autonomous

A Real-Time Speed Limit Sign Recognition System for Autonomous Vehicle Using SSD Algorithm

2021 ◽  
Author(s):  
Nur Nabilah Abu Mangshor ◽  
Nor Syahirah Saharuddin ◽  
Shafaf Ibrahim ◽  
Ahmad Firdaus Ahmad Fadzil ◽  
Khyrina Airin Fariza Abu Samah
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Recognition System ◽  
Speed Limit ◽  
Sign Recognition
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