Safe and Efficient Lane Change Maneuver for Obstacle Avoidance Inspired From Human Driving Pattern

Automated vehicle steering control has been actively researched in the automotive industries and academia over a decade. While several automotive companies and suppliers have recently demonstrated autonomous parking, lane keeping control, and lane centering control systems, automated lane change and obstacle avoidance maneuvering have not been as well demonstrated with the same level of maturity. This paper describes an algorithm that assesses environment and situation around the subject vehicle and makes a proper decision when an automated lane change or obstacle avoidance maneuvering is needed. The algorithm continuously monitors the surrounding traffic and lane markings using various types of sensors, and makes judgments along the vehicle future motion. Collision threat is evaluated by comparing the future path of the vehicle and the surrounding traffics in temporal-spatial plane. Typical driving behavior patterns are modeled to ensure safety under various scenarios. This algorithm has been implemented on a test vehicle and validated on straight and curved roads for various speeds of up to 110km/h. Several test cases have been completed and the results are provided.

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Empirical Approach for Determining Lane Change Distance at Obstacle Avoidance Manoeuvre

PROMET - Traffic&Transportation ◽

10.7307/ptt.v28i3.1890 ◽

2016 ◽

Vol 28 (3) ◽

pp. 267-275

Author(s):

Zoran Miladin Papić ◽

Goran Zovak ◽

Vuk Bogdanović ◽

Nenad Josip Saulić

Keyword(s):

Obstacle Avoidance ◽

Traffic Accident ◽

Research Study ◽

Intelligent Vehicles ◽

Theoretical Research ◽

Maximum Speed ◽

Lane Change ◽

Unique Problem ◽

Test Track ◽

Avoidance Manoeuvre

The lane change of vehicles for avoiding hitting a sudden obstacle represents a significant and unique problem for traffic accident experts. Most mathematic models for determining the lane change distance are based on theoretical research studies and a lot of simplifications and approximations. In order to analyse the influence of different drivers and vehicles on a manoeuvre, an experimental research study of lane change was carried out at the test track which enables repeatability in the same conditions. The drivers were instructed to drive through the test track at a maximum speed without displacing the traffic cones. Based on the statistical analyses of the successful lane change manoeuvres an empirical model for the calculation of lane change distance for obstacle avoidance was formed. This model can be applied in the procedure of traffic accident reconstructions as well as within the development of the concept of modern intelligent vehicles.

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Research on intelligent vehicle lane changing and obstacle avoidance control based on road adhesion coefficient

Journal of Vibration and Control ◽

10.1177/10775463211029139 ◽

2021 ◽

pp. 107754632110291

Author(s):

Kang Huang ◽

Cheng Jiang ◽

Ming-ming Qiu ◽

Di Wu ◽

Bing-zhan Zhang

Keyword(s):

Model Predictive Control ◽

Obstacle Avoidance ◽

Predictive Control ◽

Trajectory Tracking ◽

Road Surface ◽

Recursive Least Squares ◽

Intelligent Vehicle ◽

Lane Change ◽

Adhesion Coefficient ◽

The Road

Aimed at the safety and stability problems of intelligent vehicles under extreme conditions such as low adhesion road surface and emergency lane change and obstacle avoidance, this article designs a lane change and obstacle avoidance controller based on road adhesion coefficient. Using the nonlinear vehicle dynamics model as the prediction model, using the recursive least squares method to identify the road adhesion coefficient, considering the road adhesion coefficient to plan and adjust in the obstacle avoidance path as well as limit constraint conditions of the model predictive control controller, using model predictive control method for the expectation of intelligent vehicle trajectory tracking, travels tremendously guarantee the security and stability of driving. The joint CarSim–Simulink simulations results show that under poor road conditions, the trajectory tracking accuracy after optimization is higher and the vehicle is less prone to sideslip and instability. The lane change controller designed in this article has strong adaptability to different road surface adhesion coefficient, and all parameters can be controlled within a reasonable safety range at different speeds, with good robustness.

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