Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic

Liu, Wei; Xia, Xin; Liu, Xiong; Lu, Yishi; Gao, Lijing; Yu, Zhuoping

doi:10.1109/jsen.2021.3059050

Cited by 138 publications

(80 citation statements)

References 35 publications

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“…Depending on AVs’ nonlinear characteristics and parameter uncertainty, researchers in recent studies proposed some novel kinematic model-based and robust fusion methods for localization and state estimation (velocity and attitude) to ensure high accuracy and reliability by integrating different sensing and measuring units such as a global navigation satellite system (GNSS), camera, LiDAR simultaneous localization and mapping (LiDAR-SLAM), and inertial measurement unit (IMU) [ 19 ]. The sideslip angle estimation and measurement under severe conditions are one of the challenging sections of AV research in ITS where the researchers are proposed different approaches and models such as automated vehicle sideslip angle estimation considering signal measurement characteristics [ 20 ], autonomous vehicle kinematics and dynamics synthesis for sideslip angle estimation based on the consensus Kalman filter [ 21 ], vision-aided intelligent vehicle sideslip angle estimation based on a dynamic model [ 22 ], and IMU-based automated vehicle body sideslip angle and attitude estimation aided by GNSS using parallel adaptive Kalman filters [ 23 , 24 , 25 ]. The main challenges of those types of integrated fusion are high latency, measurement delay, and less reliability for long-distance communication in various driving conditions.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking

Hasanujjaman

Chowdhury

Jang

2023

Sensors

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Complete autonomous systems such as self-driving cars to ensure the high reliability and safety of humans need the most efficient combination of four-dimensional (4D) detection, exact localization, and artificial intelligent (AI) networking to establish a fully automated smart transportation system. At present, multiple integrated sensors such as light detection and ranging (LiDAR), radio detection and ranging (RADAR), and car cameras are frequently used for object detection and localization in the conventional autonomous transportation system. Moreover, the global positioning system (GPS) is used for the positioning of autonomous vehicles (AV). These individual systems’ detection, localization, and positioning efficiency are insufficient for AV systems. In addition, they do not have any reliable networking system for self-driving cars carrying us and goods on the road. Although the sensor fusion technology of car sensors came up with good efficiency for detection and location, the proposed convolutional neural networking approach will assist to achieve a higher accuracy of 4D detection, precise localization, and real-time positioning. Moreover, this work will establish a strong AI network for AV far monitoring and data transmission systems. The proposed networking system efficiency remains the same on under-sky highways as well in various tunnel roads where GPS does not work properly. For the first time, modified traffic surveillance cameras have been exploited in this conceptual paper as an external image source for AV and anchor sensing nodes to complete AI networking transportation systems. This work approaches a model that solves AVs’ fundamental detection, localization, positioning, and networking challenges with advanced image processing, sensor fusion, feathers matching, and AI networking technology. This paper also provides an experienced AI driver concept for a smart transportation system with deep learning technology.

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Section: Introductionmentioning

confidence: 99%

Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking

Hasanujjaman

Chowdhury

Jang

2023

Sensors

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“…However, these states could only be obtained through specific equipment. To this end, researchers have designed robust estimators by integrating cameras, global navigation satellite system (GNSS), and inertial measurement unit (IMU) [ 26 , 27 , 28 , 29 , 30 ]. These techniques could be used in the future whole-vehicle test verification of our proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Highly Robust Adaptive Sliding Mode Trajectory Tracking Control of Autonomous Vehicles

Xie

Liang

Chien

2023

Sensors

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Autonomous driving technology has not yet been widely adopted, in part due to the challenge of achieving high-accuracy trajectory tracking in complex and hazardous driving scenarios. To this end, we proposed an adaptive sliding mode controller optimized by an improved particle swarm optimization (PSO) algorithm. Based on the improved PSO, we also proposed an enhanced grey wolf optimization (GWO) algorithm to optimize the controller. Taking the expected trajectory and vehicle speed as inputs, the proposed control scheme calculates the tracking error based on an expanded vector field guidance law and obtains the control values, including the vehicle’s orientation angle and velocity on the basis of sliding mode control (SMC). To improve PSO, we proposed a three-stage update function for the inertial weight and a dynamic update law for the learning rates to avoid the local optimum dilemma. For the improvement in GWO, we were inspired by PSO and added speed and memory mechanisms to the GWO algorithm. Using the improved optimization algorithm, the control performance was successfully optimized. Moreover, Lyapunov’s approach is adopted to prove the stability of the proposed control schemes. Finally, the simulation shows that the proposed control scheme is able to provide more precise response, faster convergence, and better robustness in comparison with the other widely used controllers.

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“…Vehicle state and parameter estimation is an important part of vehicle dynamic control. Liu et al (2021) proposed a new estimation method of vehicle side-slip angle based on kinematic model, which integrated the information of Global Navigation Satellite System (GNSS) and inertial Measurement Unit (IMU). Xia et al (2018) proposed a method to estimate the attitude and lateral velocity of an autonomous vehicle with the assistance of vehicle dynamics using a six-degree-of-freedom IMU.…”

Section: Introductionmentioning

confidence: 99%

Research on automatic emergency steering collision avoidance and stability control of intelligent driving vehicle

Liu

Wen²,

Liu³

et al. 2023

Front. Robot. AI

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In view of the need for emergency steering to avoid collision when the vehicle is in a dangerous scene, and the stability control of the vehicle during collision avoidance. This paper proposes a planning and control framework. A path planner considering the kinematics and dynamics of the vehicle system is used to formulate the safe driving path under emergency conditions. LQR lateral control algorithm is designed to calculate the output steering wheel angle. On this basis, adaptive MPC control algorithm and four-wheel braking force distribution control algorithm are designed to achieve coordinated control of vehicle driving stability and collision avoidance safety. The simulation results show that the proposed algorithm can complete the steering collision avoidance task quickly and stably.

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Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic

Cited by 138 publications

References 35 publications

Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking

Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking

Highly Robust Adaptive Sliding Mode Trajectory Tracking Control of Autonomous Vehicles

Research on automatic emergency steering collision avoidance and stability control of intelligent driving vehicle

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