This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

https://cvrr.ucsd.edu/publications/2013/SayananTrivedi_IEEETITS2013_VehicleSurvey.pdf

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Aspects of the invention relate generally to autonomous vehicles. Specifically, the features described may be used alone or in combination in order to improve the safety, use, driver experience, and performance of these vehicles.

System and method for predicting behaviors of detected objects

Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Systems and methods are provided for controlling a vehicle A safe envelope driving pattern is determined to control the vehicle in an autonomous mode User identification data and sensor data are received from one or more sensors associated with the vehicle A driver-specific driving pattern is determined based on the received sensor data and the user identification data Operation of the vehicle is controlled in the autonomous mode based on the identification of the user in the driver's seat, the safe envelope driving pattern, and the user-specific driving pattern

Driving pattern recognition and safety control

Aspects of the disclosure relate generally to determining whether an autonomous vehicle should be driven in an autonomous or semiautonomous mode (where steering, acceleration, and braking are controlled by the vehicle's computer). For example, a computer may maneuver a vehicle in an autonomous or a semiautonomous mode. The computer may continuously receive data from one or more sensors. This data may be processed to identify objects and the characteristics of the objects. The detected objects and their respective characteristics may be compared to a traffic pattern model and detailed map information. If the characteristics of the objects deviate from the traffic pattern model or detailed map information by more than some acceptable deviation threshold value, the computer may generate an alert to inform the driver of the need to take control of the vehicle or the computer may maneuver the vehicle in order to avoid any problems.

Determining when to drive autonomously

A method for computationally predicting future movement behaviors of at least one target object can have the steps of producing sensor data by at least one sensor physically sensing the environment of a host vehicle, and computing a plurality of movement behavior alternatives of a target object sensed by the sensor(s). The context based prediction step uses a set of classifiers, each classifier estimating a probability that said sensed target object will execute a movement behavior at a time. The method can also include validating the movement behavior alternatives by a physical prediction comparing measured points with trajectories of situation models and determining at least one trajectory indicating at least one possible behavior of the traffic participant, estimating at least one future position of the traffic participant based on the at least one trajectory, and outputting a signal representing the estimate future position.

A method and system for predicting movement behavior of a target traffic object

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures. Such measures can be aided by the development of suitable machine learning (ML) models that predict, for example, the evolution of the collision risk over time. In October 2019, in an attempt to study this opportunity, the European Space Agency released a large curated dataset containing information about close approach events in the form of conjunction data messages (CDMs), which was collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, which was an ML competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying ML methods to this problem domain.

/pdf/spacecraft-collision-avoidance-challenge-design-and-results-4xit5jb3yg.pdf

Spacecraft collision avoidance challenge: Design and results of a machine learning competition

Estimating the dynamic states of a real-world object over time using a camera, two dimensional (2D) image information and a combination of different measurements of the distance between the object and the camera. The 2D image information is used to track a 2D position of the object as well as its 2D size of the appearance and change in the 2D size of the appearance of the object. In addition, the distance between the object and the camera is obtained from one or more direct depth measurements. The 2D position, the 2D size, and the depth of the object are coupled to obtain an improved estimation of three dimensional (3D) position and 3D velocity of the object. The object tracking apparatus uses the improved estimation to track real-world objects. The object tracking apparatus may be used on a moving platform such as a robot or a car with mounted cameras for a dynamic visual scene analysis.

Visually tracking an object in real world using 2D appearance and multicue depth estimations

A method is proposed for detecting the position of a target object (51) in the environment of a host vehicle, comprising the following steps: - receiving a first position (6) of the target object as detected by an object position sensor (2) like e.g. a radar sensor, - receiving an image (7) containing the target object as detected by an image sensor (3) like e.g. a camera, - projecting the first position (6) on the image (7), and - refining the first position (6) by computing a second position (25) of the target object on the basis of a symmetry search within the image (7).

Vehicle with improved traffic-object position detection

In an intelligent forward collision warning system that takes into account not only a first preceding vehicle traveling immediately ahead of the ego vehicle but also a second preceding vehicle traveling immediately ahead of the first preceding vehicle, a warning signal is produced upon detecting a critical state of the first or second preceding vehicle or an external condition that is predicted to cause the first preceding vehicle and the ego vehicle to be in a same lane with the first time to collision (TTC) below a prescribed value.

Intelligent forward collision warning system

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