Perception, Planning and Control for Self-Driving System Based on On-board Sensors

Dai, Yanyan; Lee, Suk-Gyu

doi:10.1177/1687814020956494

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…LiDAR-SLAM or Visual SLAM uses a monocular or stereo camera to track the features of consecutive images while estimating the relative orientation and translation. SLAM is used in AVs for Motion Control, Path Planning, and sometimes even pedestrian detection [13]. K-Means: It is an unsupervised algorithm that groups unlabeled or unclassified datasets into predefined clusters.…”

Section: Architectures and Algorithmsmentioning

confidence: 99%

“…In this paper, we will look at some of the different path planning algorithms developed over time and the different approaches used. In a paper titled "Perception, Planning and Control for Self-Driving System Based on On-board Sensors," a real-time lane detection system is proposed, with vision system functions as the primary base [13]. They use a deep learning algorithm based on CNN.…”

Section: Path Planningmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Autonomous Vehicles: Progress, Methods and Challenges

et al. 2022

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Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities.

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Section: Architectures and Algorithmsmentioning

confidence: 99%

Section: Path Planningmentioning

confidence: 99%

A Review on Autonomous Vehicles: Progress, Methods and Challenges

et al. 2022

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“…Autonomous driving technology is rapidly developing, which has been attributed to advances in the sensor, communications and computer industries [1][2][3][4][5][6][7]. As the first step in autonomous driving, the results of environmental perception systems directly affect the subsequent motion planning and decision making.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

et al. 2022

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Robust and precise vehicle detection is the prerequisite for decision-making and motion planning in autonomous driving. Vehicle detection algorithms follow three steps: ground segmentation, obstacle clustering and bounding box fitting. The ground segmentation result directly affects the input of the subsequent obstacle clustering algorithms. Aiming at the problems of over-segmentation and under-segmentation in traditional ground segmentation algorithms, a ground segmentation algorithm based on Gaussian process is proposed in this paper. To ensure accurate search of real ground candidate points as training data for Gaussian process, the proposed algorithm introduces the height and slope criteria, which is more reasonable than the use of fixed height threshold for searching. After that, a sparse covariance function is introduced as the kernel function for calculation in Gaussian process. This function is more suitable for ground segmentation situation the radial basis function (RBF). The proposed algorithm is tested on our autonomous driving experimental platform and the public autonomous driving dataset KITTI, compared with the most used RANSAC algorithm and ray ground filter algorithm. Experiment results show that the proposed algorithm can avoid obvious over-segmentation and under-segmentation. In addition, compared with the RBF, the introduction of the sparse covariance function also reduces the computation time by 37.26%.

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“…9,10 However, these proposed approaches are tested in a particular scenario and the speed does not exceed 35 km/h. A fuzzy sliding mode control is proposed for the vehicle steering actuation at constant speed in Dai and Lee 11 while a neural network is used to adjust the control gain as well as realize the variable gain sliding mode control in Wan et al 12 Model-based controllers may be applied in combination with PID, like in Levinson et al, 13 Marcano et al, 14 and Feraco et al, 15 where longitudinal and lateral guidance are simultaneously achieved using a Model Predictive Control (MPC) and a PID. MPC is mostly applied in assisted and automated vehicles since it can handle effectively Multi-Input Multi-Output (MIMO) systems with input and state constraints.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control for comfort optimization in assisted and driverless vehicles

Luciani

Bonfitto

Amati

et al. 2020

Advances in Mechanical Engineering

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This paper presents a method to design a Model Predictive Control to maximize the passengers’ comfort in assisted and self-driving vehicles by achieving lateral and longitudinal dynamic. The weighting parameters of the MPC are tuned off-line using a Genetic Algorithm to simultaneously maximize the control performance in the tracking of speed profile, lateral deviation and relative yaw angle and to optimize the comfort perceived by the passengers. To this end, two comfort evaluation indexes extracted by ISO 2631 are used to evaluate the amount of vibration transmitted to the passengers and the probability to experience motion sickness. The effectiveness of the method is demonstrated using simulated experiments conducted on a subcompact crossover vehicle. The control tracking performance produces errors lower than 0.1 m for lateral deviation, 0.5° for relative yaw angle and 1.5 km/h for the vehicle speed. The comfort maximization results in a low percentage of people who may experience nausea (below 5%) and in a low value of equivalent acceleration perceived by the passenger (below 0.315 [Formula: see text]“not uncomfortable” by ISO 2631). The robustness at variations of vehicle parameters, namely vehicle mass, front and rear cornering stiffness and mass distribution, is evaluated through a sensitivity analysis.

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Perception, Planning and Control for Self-Driving System Based on On-board Sensors

Cited by 16 publications

References 13 publications

A Review on Autonomous Vehicles: Progress, Methods and Challenges

A Review on Autonomous Vehicles: Progress, Methods and Challenges

A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

Model predictive control for comfort optimization in assisted and driverless vehicles

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