Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier, Matthias

doi:10.1515/auto-2016-0129

Cited by 31 publications

(13 citation statements)

References 184 publications

(429 reference statements)

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“…As they do not match the real trajectory in predictions of more than 1 second, such methods may not be effective in practical applications. Trajectory prediction methods based on machine learning mainly include Gaussian process, hidden Markov model, or Bayesian network [13][14][15]. Tran et al [16][17][18] learned the model parameters of vehicle trajectory through a Gaussian process, and Patterson et al [19,20] used a mixed Gaussian model to learn the vehicle trajectory generation model.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

Ruibin

Guo

Long

et al. 2022

Journal of Advanced Transportation

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A vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. The algorithm analyzes the characteristics of object motion that are affected by the surrounding environment and the interaction of nearby objects and is based on the complex traffic environment perception dual multiline light detection and ranging (LiDAR) technology. The time sequence aerial view map and time sequence front view depth map are obtained using real-time point cloud information perceived by the LiDAR. Time sequence high-level abstract combination features in the multiview scene are then extracted by an improved VGG19 network model and are fused with the potential spatiotemporal interaction of the multitarget operation state data extraction features detected by the laser radar by using a one-dimensional convolution neural network. A temporal feature vector is constructed as the input data of the bidirectional long-term and short-term memory (BiLSTM) network, and the desired input-output mapping relationship is trained to predict the motion state of traffic participants. According to the test results, the proposed BiLSTM model based on point cloud multiview and vehicle interaction information is better than other methods in predicting the state of target vehicles. The results can provide support for the research to evaluate the risk of intelligent vehicle operation environment.

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Section: Introductionmentioning

confidence: 99%

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

Ruibin

Guo

Long

et al. 2022

Journal of Advanced Transportation

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“…For localization of the host vehicle as well as surrounding vehicles in IDASs, Schreier et al [ 14 ] describe an integrated approach that predicts trajectories based on a maneuver estimation model. Driving maneuvers are inferred for each vehicle with a Bayesian network.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning

Dailey

2021

Sensors

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Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle’s trajectory using sparse optical flow and tracks vehicles in the surrounding environment using convolutional neural networks. Optical flow is used to measure the linear and angular velocity of the host vehicle. The convolutional neural networks are used to measure target vehicles’ positions relative to the host vehicle using image-based detections. Finally, the system fuses host and target vehicle trajectories in the world coordinate system using the velocity of the host vehicle and the target vehicles’ relative positions with the aid of an Extended Kalman Filter (EKF). We implement and test our model quantitatively in simulation and qualitatively on real-world test video. The results show that the algorithm is superior to state-of-the-art sequential state estimation methods such as visual SLAM in performing accurate global localization and trajectory estimation for host and target vehicles.

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“…The node range vision fusion collects the detected objects from multiple DNNs and outputs the fused results. The node imm ukf tracker uses the IMM-UKF-PDA tracking algorithm [37] which utilizes three combined Bayesian filters to simultaneously tackle association uncertainties, motion uncertainties and estimate non-linear stochastic motion model in real-time. The node native motion predictor generates predicted angles and velocities [23] from which the node costmap generator creates an obstacle map to show the passable probability around the car.…”

Section: B Adapp: a Set Of Level-4 Autonomous Driving Applicationsmentioning

confidence: 99%

Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card

Sung,

Xu,

Guan

et al. 2021

Preprint

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Autonomous driving is of great interest in both research and industry. The high cost has been one of the major roadblocks that slow down the development and adoption of autonomous driving in practice. This paper, for the first-time, shows that it is possible to run level-4 (i.e., fully autonomous driving) software on a single off-the-shelf card (Jetson AGX Xavier) for less than $1k, an order of magnitude less than the state-of-the-art systems, while meeting all the requirements of latency. The success comes from the resolution of some important issues shared by existing practices through a series of measures and innovations. The study overturns the common perceptions of the computing resources required by level-4 autonomous driving, points out a promising path for the industry to lower the cost, and suggests a number of research opportunities for rethinking the architecture, software design, and optimizations of autonomous driving.The explorations together lead to the success of making all of the six level-4 autonomous driving applications achieve real-time performance on a single Jetson card. The success has multi-fold implications. It entails the need for the industry and the research community to reexamine some assumptions (on architecture, power budget, cost, the impact of interference,

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Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Cited by 31 publications

References 184 publications

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning

Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card

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