Abstract: A method, system and apparatus for recognizing continuous driving style are provided, which involve the intelligent vehicles field. The method comprises: collecting multi-dimensional driving data from a plurality of drivers in daily driving scenarios, segmenting the driving data to obtain a plurality of driving segments, calculating statistical features of each driving segment to determine high-dimensional continuous driving statistical features, reducing dimensionality of the driving statistical features to generate common factors for each driving segment, representing each driving segment with a driving word based on the common factors, representing all the driving segments of a target driver as a driving word sequence, constructing a hierarchical latent model of driving behavior, and inputting the driving word sequence into the hierarchical latent model of driving behavior to determine the continuous driving style of the target driver.

Abstract: An auxiliary support device for a rolling-cut shear, including: a lower cutting table; a plurality of first hydraulic support cylinders, a plurality of support plates, a plurality of second hydraulic support cylinders shorter than the first hydraulic support cylinders, a slide plate, and a plurality of horizontal hydraulic cylinders. The lower cutting table includes a first front end including a first mounting groove, and a second mounting groove, and an inner plate. The bottom wall of the second mounting groove includes a plurality of first bolt holes and a plurality of first recesses. The inner plate is embedded in the second mounting groove through a plurality of bolts passing through the plurality of first bolt holes. The inner plate includes a plurality of second bolt holes and a plurality of second recesses. The plurality of support plates lean against the inner plate.

Abstract: In one embodiment, example systems and methods related to a manner of unifying heterogeneous datasets are provided. Multiple heterogeneous datasets containing traffic or driving data are collected. The records of the datasets are combined, and the records in the combined dataset are ordered into a plurality of time series based on timestamps associated with each record. A Bayesian learning method, such as hidden Markov models, is used to identify traffic primitives in the datasets. Each traffic primitive may include several consecutive records in the combined dataset and may correspond to particular driving actions such as turning left or right, stopping, accelerating, etc. The traffic primitives are used to create a traffic primitive index that can be queried by users or researchers for specific records. These records can be used to train or test one or more learning-based algorithms.

Abstract: In one embodiment, example systems and methods related to a manner of optimizing LiDAR sensor placement on autonomous vehicles are provided. A range-of-interest is defined for the autonomous vehicle that includes the distances from which the autonomous vehicle is interested in collecting sensor data. The range-of-interest is segmented into multiple cubes of the same size. For each LiDAR sensor, a shape is determined based on information such as the number of lasers in each LiDAR sensor and the angle associated with each laser. An optimization problem is solved using the determined shape for each LiDAR sensor and the cubes of the range-of-interest to determine the locations to place each LiDAR sensor to maximize the number of cubes that are captured. The optimization problem may further determine the optimal pitch angle and roll angle to use for each LiDAR sensor to maximize the number of cubes that are captured.

Abstract: The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a driving encounter recognition system. The method includes receiving information, from one or more sensors coupled to a first vehicle, determining first trajectory information associated with the first vehicle and second trajectory information associated with a second vehicle, extracting a feature vector, providing the feature vector to a trained classifier, the classifier trained using unsupervised learning based on a plurality of feature vectors, and receiving, from the trained classifier, a classification of the current driving encounter in order to facilitate the first vehicle to perform a maneuver based on the current driving encounter.

Abstract: In one embodiment, example systems and methods related to a manner of optimizing LiDAR sensor placement on autonomous vehicles are provided. A range-of-interest is defined for the autonomous vehicle that includes the distances from which the autonomous vehicle is interested in collecting sensor data. The range-of-interest is segmented into multiple cubes of the same size. For each LiDAR sensor, a shape is determined based on information such as the number of lasers in each LiDAR sensor and the angle associated with each laser. An optimization problem is solved using the determined shape for each LiDAR sensor and the cubes of the range-of-interest to determine the locations to place each LiDAR sensor to maximize the number of cubes that are captured. The optimization problem may further determine the optimal pitch angle and roll angle to use for each LiDAR sensor to maximize the number of cubes that are captured.

Abstract: In one embodiment, example systems and methods related to a manner of unifying heterogeneous datasets are provided. Multiple heterogeneous datasets containing traffic or driving data are collected. The records of the datasets are combined, and the records in the combined dataset are ordered into a plurality of time series based on timestamps associated with each record. A Bayesian learning method, such as hidden Markov models, is used to identify traffic primitives in the datasets. Each traffic primitive may include several consecutive records in the combined dataset and may correspond to particular driving actions such as turning left or right, stopping, accelerating, etc. The traffic primitives are used to create a traffic primitive index that can be queried by users or researchers for specific records. These records can be used to train or test one or more learning-based algorithms.

Abstract: The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a driving encounter recognition system. The method includes receiving information, from one or more sensors coupled to a first vehicle, determining first trajectory information associated with the first vehicle and second trajectory information associated with a second vehicle, extracting a feature vector, providing the feature vector to a trained classifier, the classifier trained using unsupervised learning based on a plurality of feature vectors, and receiving, from the trained classifier, a classification of the current driving encounter in order to facilitate the first vehicle to perform a maneuver based on the current driving encounter.

Abstract: An intelligent driving system with an embedded driver model. The system includes a driver model module that adjusts vehicle performances according to driving characteristics of a driver and road environment. A driver's visual and tactile information may be taken into account when driving a vehicle, so as to tune vehicle performances to allow the vehicle to adapt itself to the individual driver.

Abstract: The present application discloses an intelligent driving system with an embedded driver model. The system includes a driver model module that can tune vehicle performances according to driving characteristics of a driver and road environment. Applying the system provided by the present application to vehicle control systems, the driver's visual and tactile information may be taken into account when driving a vehicle, so as to tune vehicle performances to allow the vehicle to adapt itself to the individual driver.