Trajectory optimization of an electric vehicle with minimum energy consumption using inverse dynamics model and servo constraints

. Air pollution, energy consumption, and human safety
issues have aroused people's concern around the world. This phenomenon could
be significantly alleviated with the development of automatic driving
techniques, artificial intelligence, and computer science. Autonomous
vehicles can be generally modularized as environment perception, path
planning, and trajectory tracking. Trajectory tracking is a fundamental part
of autonomous vehicles which controls the autonomous vehicles effectively
and stably to track the reference trajectory that is predetermined by the
path planning module. In this paper, a review of the state-of-the-art trajectory
tracking of autonomous vehicles is presented. Both the trajectory tracking
methods and the most commonly used trajectory tracking controllers of
autonomous vehicles, besides state-of-art research studies of these controllers,
are described.

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Review article: State-of-the-art trajectory tracking of autonomous vehicles

Path tracking system plays a key technology in autonomous driving. The system should be driven accurately along the lane and be careful not to cause any inconvenience to passengers. To address such tasks, this research proposes hybrid tracker based optimal path tracking system. By applying a deep learning based lane detection algorithm and a designated fast lane fitting algorithm, this research developed a lane processing algorithm that shows a match rate with actual lanes with minimal computational cost. In addition, three modified path tracking algorithms were designed using the GPS based path or the vision based path. In the driving system, a match rate for the correct ideal path does not necessarily represent driving stability. This research proposes hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments. The driving stability has been studied in complex road environments such as straight road with multiple 3-way junctions, roundabouts, intersections, and tunnels. Consequently, the proposed system experimentally showed the high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions. Code will be available in https://github.com/DGIST-ARTIV .

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Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments

In recent years vehicles with high level of automation are becoming more popular which is expected to continue in the future. Advanced driving assistance systems are increasingly taking control of the vehicle, starting with the support of the driving task. Automated or highly automated vehicles are expected to follow a planned road safely. In this paper, the authors aim to improve the accuracy of path following by developing a new control strategy. The controller includes both pure pursuit and Stanley methods, the operation of the controller is based on the geometry of the vehicle and the path; the key is the proper weighting between two controllers. The performance of the combined path following controller was measured on a demonstration vehicle.

Path following controller for autonomous vehicles

This paper proposes a solution for one of the most important components of autonomous driving: "overtaking maneuver". Follow the Gap method (FGM) is oneof the most popular obstacle avoidance algorithms and directly obtains steering angle from position of the obstacles around. This paper is the first study using FGM to solve the overtaking problem. Different from previous studies where a trajectory is planned and then a controller is designed to track it; we use FGM for motion planning and control together. This paper focuses on overtaking maneuver in a challenging environment like highway traffic where the safety and fast response are the key factors. After we adapt the FGM to overtaking problem, we compare it with an existing overtaking method X-sin functions from literature. Since X-sin functions method requires a path tracker (controller), Stanley method is combined with X-sin functions. At the end of this work, we show several advantages of FGM comparing to the X-sin functions based overtaking approach.

Autonomous Overtaking Maneuver Design based on Follow the Gap Method.

This paper proposes a geometric model based lateral control system for a ground vehicle. Lateral control algorithm takes the advantages of two different path tracking methods at different path geometries. Two of the well-known geometric path tracking methods, namely Pure-Pursuit method and Stanley method, are combined with a simple and easy to implement approach. Pure-Pursuit method is very good at low speeds. However, as speed increases cutting corner behavior occurs and vehicle tends to converge to path relatively slow. In contrast, Stanley method convergence to the road is very fast and there is no cutting corner behavior. However since there is no look ahead behavior in Stanley method, it tends to overshoot from the desired path for sharp turns. In this work we propose a hybrid method to improve convergence to the path, prevent cutting corner and overshoot from the desired path.

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Hybrid controller approach for an autonomous ground vehicle path tracking problem

Vehicle autonomy definitionally is the act of processing information gathered from the environment and acting on the decisions formed based on this information. Therefore, any autonomous paradigm can only perform as good as the quality of the information it can understand. Lane identification forms the foundation of many of the autonomous drive and driver-assist technologies. However, current methods are not always reliable, especially under the edge-cases. In this paper, we have experimentally evaluated and extended the state-of-the-art deterministic lane detection methods. Our evaluation provides experimental evidence towards their efficacy in extreme cases: real-data with sharp shadows and varying lighting that is recorded through a camera that has a limited field of view. Experimental results suggest that a method that builds similarly to human perception performs better—with an increase of 32% in its accuracy. Our hypothesis is that autonomous vehicles that can perform even under these extreme conditions will play an important role on the fully autonomous systems.

Detecting Road Lanes under Extreme Conditions: A Quantitative Performance Evaluation

In this work a Laguerre Orthonormal Basis Functions Based Model Predictive Control (MPC) approach is proposed for automotive Adaptive Cruise Control (ACC) application in order to reduce optimization problem complexity. Model of ACC system is constructed using ego vehicle and inter-vehicular dynamics. For inter-vehicular distance control Constant Time Gap Policy is derived and to achieve the similarity with real world driver, an empirical driver model is utilized. Both approaches are integrated into the problem formulation. To avoid the effects of unmeasured disturbances on vehicle following performance, integral action is added to the system. Classical MPC approach is reformulated by representing control signal as sum of Laguerre Basis functions. Distance tracking error and control signal change is constrained to take safety measure and to cope with the system limitations. Additionally, to prevent infeasibility, slack variable approach is utilized. Classical MPC and proposed Laguerre MPC controllers are compared in terms of distance tracking performance and problem complexity.

Umut Karapinar

Papers

Hybrid controller approach for an autonomous ground vehicle path tracking problem

Detecting Road Lanes under Extreme Conditions: A Quantitative Performance Evaluation

Laguerre MPC Formulation for Automotive Adaptive Cruise Control Application and Performance Analysis