Highway Traffic Scenario-Based Lane Change Strategy for Autonomous Vehicle

2018 ◽  
pp. 405-413
Author(s):  
Gourish Hiremath ◽  
Kiran Wani ◽  
Sanjay Patil
Keyword(s):  
Autonomous Vehicle ◽  
Lane Change ◽  
Highway Traffic ◽  
Change Strategy ◽  
Traffic Scenario

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.

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.

scholarly journals Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

2018 ◽  
Vol 233 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Armin Norouzi ◽  
Milad Masoumi ◽  
Ali Barari ◽  
Saina Farrokhpour Sani
Keyword(s):  
Optimization Algorithm ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Lane Change ◽  
Lateral Control ◽  
Lane Changing ◽  
Dynamic Constraints ◽  
Bicycle Model ◽  
Double Lane Change ◽  
Backstepping Controller

In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

scholarly journals Model predictive control–based cooperative lane change strategy for improving traffic flow

2016 ◽  
Vol 8 (2) ◽  
pp. 168781401663299 ◽  
Author(s):  
Di Wang ◽  
Manjiang Hu ◽  
Yunpeng Wang ◽  
Jianqiang Wang ◽  
Hongmao Qin ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Traffic Flow ◽  
Predictive Control ◽  
Lane Change ◽  
Change Strategy

scholarly journals Evaluation of Integrated Variable Speed Limit and Lane Change Control for Highway Traffic Flow

2021 ◽  
Vol 54 (2) ◽  
pp. 107-113
Author(s):  
Tianchen Yuan ◽  
Faisal Alasiri ◽  
Yihang Zhang ◽  
Petros A. Ioannou
Keyword(s):  
Traffic Flow ◽  
Speed Limit ◽  
Lane Change ◽  
Variable Speed ◽  
Highway Traffic ◽  
Variable Speed Limit

scholarly journals Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles

2021 ◽  
Author(s):  
Fei Ye ◽  
Pin Wang ◽  
Ching-Yao Chan ◽  
Jiucai Zhang
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Lane Change ◽  
Change Strategy
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