Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Data-Driven Intelligent Transportation Systems: A Survey

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

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

A brake system control for use in a vehicle with four wheels comprising the steps of: determining a desired yaw rate (454); determining a yaw torque command responsive to the desired yaw rate (806); if the vehicle is in an anti-lock braking mode during driver commanded braking, applying the yaw torque command to only one of the four wheels to release brake pressure in said one of the four wheels (258-266, 274, 278, 280, 410-418); if the vehicle is in a positive acceleration traction control mode during driver commanded acceleration, applying the yaw torque command to only one of the four wheels to apply brake pressure in said one of the four wheels (258-266, 288-292, 410-418); and if the vehicle is not in the anti-lock braking mode or in the positive acceleration traction control mode, then: (i) determining whether a vehicle brake pedal is depressed (370); (ii) if the vehicle brake pedal is depressed, applying brake force to the vehicle wheels responsive to the depression of the brake pedal (374, 412, 418), wherein the applied brake force is modified to at least two of the vehicle wheels to create a left-right brake torque differential responsive to the yaw torque command.

Brake control system

A vehicle includes vehicle systems each having driver-selectable vehicle system settings (VSS), and a control system for statistically modeling the VSS to determine an identity of a driver. The control system automatically controls a setting of at least one of the vehicle systems using or based on the identity. The control system statistically models the VSS for the driver over time to produce a historical driver profile (HDP) for the driver, and can automatically update the HDP when said driver manually changes any one of the VSS. An optional driver identification device can verify the identity. A method for controlling a predetermined onboard system of a vehicle includes collecting a set of VSS for a plurality of onboard systems, processing the VSS through a statistical modeling algorithm to determine an identity of a driver of the vehicle, and automatically controlling a predetermined onboard system using the identity of the driver.

Apparatus and Method for Vehicle Driver Recognition and Customization Using Onboard Vehicle System Settings

A system and method for identifying a person as an authorized driver of a vehicle based on settings of the vehicle. The system includes a sensor for detecting a presence of a driver in the vehicle. The system also includes one or more control modules corresponding to the parameters of the vehicle. The parameters can be controlled by the control modules. The system also includes a driver ID controller coupled to the control modules and the sensor. The driver ID controller is configured to recognize the driver as an authorized driver based on inputs from the sensor and the control modules. The driver ID controller can also command the control units to adjust the parameters to predefined one or more attributes corresponding to the authorized driver.

Vehicle-setting-based driver identification system

An adaptive vehicle control system that classifies a driver's driving style based on characteristic curve-handling maneuvers and road and traffic conditions. The system includes a plurality of vehicle sensors that detect various vehicle parameters. A maneuver identification processor receives the sensor signals to identify a characteristic maneuver of the vehicle and provides a maneuver identifier signal of the maneuver. The system also includes a traffic and road condition recognition processor that receives the sensor signals, and provides traffic condition signals identifying traffic conditions and road condition signals identifying road conditions. A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the traffic and road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle.

Adaptive vehicle control system with driving style recognition

A method is disclosed for controlling a backing maneuver of an automotive vehicle and trailer combination in which the vehicle has operator-actuated front wheel steering and microprocessor-actuated, reversible electric motor driven rear wheel steering. For a given initial alignment of vehicle and trailer, the computer-executed method first determines whether the driver needs to pull forward before commencing the backing operation. The driver is then requested to turn the front wheels in a direction suitable for backing the vehicle without a trailer in the desired direction. The process then determines whether the driver needs to perform counter front wheel steering before backing. Then the process controls the steering of the rear wheels during the backing operation.

Yuen-Kwok Chin

Papers

Brake control system

Apparatus and Method for Vehicle Driver Recognition and Customization Using Onboard Vehicle System Settings

Vehicle-setting-based driver identification system

Adaptive vehicle control system with driving style recognition

Vehicle-trailer backing-up control system with vehicle rear wheel steering