Abstract: The present invention is related to processes for preparing a composition comprising an enantiomeric excess of a substituted pyrrolo[2,3-d]pyrimidine utilizing chiral acids.

Abstract: The present disclosure is related to solid forms of ruxolitinib di-hydrate and ruxolitinib free base, process of preparing the same, and compositions comprising the same.

Abstract: The present invention relates to salt forms of the Pim kinase inhibitor N—{(7R)-4-[(3R,4R,5S)-3-amino-4-hydroxy-5-methylpiperidin-1-yl]-7-hydroxy-6,7-dihydro-5H-cyclopenta[b]pyridin-3-yl}-6-(2,6-difluorophenyl)-5-fluoropyridine-2-carboxamide, including methods of preparation thereof, and intermediates in the preparation thereof, where the compound is useful in the treatment of Pim kinase-related diseases such as cancer.

Abstract: In one embodiment, a process is performed during controlling Autonomous Driving Vehicle (ADV). A confidence level associated with a sensed obstacle is determined. If the confidence level is below a confidence threshold, and a distance between the ADV and a potential point of contact with the sensed obstacle is below a distance threshold, then performance of a driving decision is delayed. Otherwise, the driving decision is performed to reduce risk of contact with the sensed obstacle.

Abstract: An obstacle state evolution of a spatial position of a moving obstacle over a period of time is determined. A lane-obstacle relation evolution of the moving obstacle with each of one or more lanes near the moving obstacle over the period of time is further determined. An intended movement of the moving obstacle is predicted based on the obstacle state evolution and the lane-obstacle evolution. Thereafter, a trajectory of the ADV is planned to control the ADV to avoid a collision with the moving obstacle based on the predicted intended movement of the moving obstacle. The above process is iteratively performed for each of the moving obstacles detected within a predetermined proximity of the ADV.

Abstract: In one embodiment, an autonomous driving system of an ADV perceives a driving environment surrounding the ADV based on sensor data obtained from various sensors, including detecting one or more lanes and at least a moving obstacle or moving object. For each of the lanes identified, an NN lane feature encoder is applied to the lane information of the lane to extract a set of lane features. For a given moving obstacle, an NN obstacle feature encoder is applied to the obstacle information of the obstacle to extract a set of obstacle features. Thereafter, a lane selection predictive model is applied to the lane features of each lane and the obstacle features of the moving obstacle to predict which of the lanes the moving obstacle intends to select.

Abstract: According to one embodiment, during a first planning cycle, a first lane boundary of a driving environment perceived by an ADV is determined using a first lane boundary determination scheme (e.g., current lane boundary), which has been designated as a current lane boundary determination scheme. A first trajectory is planned based on the first lane boundary to drive the ADV to navigate through the driving environment. The first trajectory is evaluated against a predetermined set of safety rules (e.g., whether it will collide or get too close to an object) to avoid a collision with an object detected in the driving environment. In response to determining that the first trajectory fails to satisfy the safety rules, a second lane determination boundary of the driving environment is determined using a second lane boundary determination scheme and a second trajectory is planned based on the second lane boundary to drive the ADV.

Abstract: Biomedical implants in accordance with various embodiments of the invention can be implemented in many different ways. The implants can be configured to receive power and transmit data, both wirelessly and simultaneously. Such devices can be configured to receive power from an external source and transmit data, such as but not limited to recorded neural data and/or other biological data, to outside the body. In many cases, the data is transmitted to the device that delivers power to the implant. For example, the power and data transmission system can be implemented with a pair of transceivers. The implant transceiver can receive power wirelessly though an external transceiver while simultaneously transmitting data to the external transceiver. In several embodiments, both forward (power) and reverse (data) links use the same pair of inductive coils in the transceivers, one coil mounted in the implant and the other in the external unit.

Abstract: A moving obstacle such as a vehicle within a proximity of an intersection and one or more exits of the intersection are identified. An obstacle state evolution of a spatial position of the moving obstacle over a period of time is determined. For each of the exits, an intersection exit encoding of the exit is determined based on intersection exit features of the exit. An aggregated exit encoding based on aggregating all of the intersection exit encodings for the exits is determined. For each of the exits, an exit probability of the exit that the moving obstacle likely exits the intersection through the exit is determined based on the obstacle state evolution and the aggregated exit encoding. Thereafter, a trajectory of the ADV is planned to control the ADV to avoid a collision with the moving obstacle based on the exit probabilities of the exits.

Abstract: In response to perceiving a moving object, one or more possible object paths of the moving object are determined based on the prior movement predictions of the moving object, for example, using a machine-learning model, which may be created based on a large amount of driving statistics of different vehicles. For each of the possible object paths, a set of trajectory candidates is generated based on a set of predetermined accelerations. Each of the trajectory candidates corresponds to one of the predetermined accelerations. A trajectory cost is calculated for each of the trajectory candidates using a predetermined cost function. One of the trajectory candidates having the lowest trajectory cost amongst the trajectory candidates is selected. An ADV path is planned to navigate the ADV to avoid collision with the moving object based on the lowest costs of the possible object paths of the moving object.

Abstract: In one embodiment, a set of predetermined driving parameters is determined from a set of driving statistics data collected from a number of vehicles, which may be driven by human drivers. For each pair of the predetermined driving parameters, a distribution of the pair of driving parameters is plotted based on their relationship on a two-dimensional (2D) distribution space. The 2D distribution space is partitioned into a number of grid cells, each grid cell representing a particular pair of driving parameters. For each of the grid cells, a probability is calculated that the pair of driving parameter likely falls in the grid cell. A grid table is generated corresponding to the pair of driving parameters. The grid table can be utilized during the autonomous driving at real-time or during simulation to determine a ride stability of an autonomous driving vehicle (ADV) in view of the pair of driving parameters.

Abstract: A moving object such as a vehicle is identified within an intersection having multiple exits. The moving object and the intersection and its exits may be identified based on sensor data obtained from various sensors mounted on an ADV. An exit coordinate map is generated based on the orientation of the moving object and a relative position of each of the exits of the intersection with respect to the current position of the moving object. For each of the exits, an exit probability of the exit that the moving object likely exits the intersection using the exit coordinate map. Thereafter, a trajectory of the ADV is planned to navigate through the intersection to avoid the collision with the moving object based on the exit probabilities of the exits of the intersection. The above process is iteratively performed for each of the moving objects detected within the proximity of the intersection.

Abstract: A system and method for predicting the near-term trajectory of a moving obstacle sensed by an autonomous driving vehicle (ADV) is disclosed. The method applies neural networks such as a LSTM model to learn dynamic features of the moving obstacle's motion based on its past trajectory up to its current position and a CNN model to learn the semantic map features of the driving environment in a portion of an image map. From the learned dynamic features of the moving obstacle and the learned semantic map features of the environment, the method applies a neural network to iteratively predict the moving obstacle's positions for successive time points of a prediction interval. To predict the moving obstacle's position at the next time point from the currently predicted position, the methods may update the learned dynamic features of the moving obstacle based on its past trajectory up to the currently predicted position.

Abstract: A driving environment is perceived based on sensor data obtained from a plurality of sensors mounted on the ADV. In response to a request for changing lane from a first lane to a second lane, path planning is performed. The path planning includes identifying a first lane change point for the ADV to change from the first lane to the second lane in a first trajectory of the ADV, determining a lane change preparation distance with respect to the first lane change point, and generating a second trajectory based on the lane change preparation distance, where the second trajectory having a second lane change point delayed from the first lane change point. Speed planning is performed on the second trajectory to control the ADV to change lane according to the second trajectory with different speeds at different point in time.

Abstract: A system and method for predicting the near-term trajectory of a moving obstacle sensed by an autonomous driving vehicle (ADV) is disclosed. The method applies neural networks such as a LSTM model to learn dynamic features of the moving obstacle's motion based on its past trajectory up to its current position and a CNN model to learn the semantic map features of the driving environment in a portion of an image map. From the learned dynamic features of the moving obstacle and the learned semantic map features of the environment, the method applies a neural network to iteratively predict the moving obstacle's positions for successive time points of a prediction interval. To predict the moving obstacle's position at the next time point from the currently predicted position, the methods may update the learned dynamic features of the moving obstacle based on its past trajectory up to the currently predicted position.

Abstract: An obstacle state evolution of a spatial position of a moving obstacle over a period of time is determined. A lane-obstacle relation evolution of the moving obstacle with each of one or more lanes near the moving obstacle over the period of time is further determined. An intended movement of the moving obstacle is predicted based on the obstacle state evolution and the lane-obstacle evolution. Thereafter, a trajectory of the ADV is planned to control the ADV to avoid a collision with the moving obstacle based on the predicted intended movement of the moving obstacle. The above process is iteratively performed for each of the moving obstacles detected within a predetermined proximity of the ADV.

Abstract: A moving obstacle such as a vehicle within a proximity of an intersection and one or more exits of the intersection are identified. An obstacle state evolution of a spatial position of the moving obstacle over a period of time is determined. For each of the exits, an intersection exit encoding of the exit is determined based on intersection exit features of the exit. An aggregated exit encoding based on aggregating all of the intersection exit encodings for the exits is determined. For each of the exits, an exit probability of the exit that the moving obstacle likely exits the intersection through the exit is determined based on the obstacle state evolution and the aggregated exit encoding. Thereafter, a trajectory of the ADV is planned to control the ADV to avoid a collision with the moving obstacle based on the exit probabilities of the exits.

Abstract: A driving environment is perceived based on sensor data obtained from a plurality of sensors mounted on the ADV. In response to a request for changing lane from a first lane to a second lane, path planning is performed. The path planning includes identifying a first lane change point for the ADV to change from the first lane to the second lane in a first trajectory of the ADV, determining a lane change preparation distance with respect to the first lane change point, and generating a second trajectory based on the lane change preparation distance, where the second trajectory having a second lane change point delayed from the first lane change point. Speed planning is performed on the second trajectory to control the ADV to change lane according to the second trajectory with different speeds at different point in time.

Abstract: In one embodiment, a set of predetermined driving parameters is determined from a set of driving statistics data collected from a number of vehicles, which may be driven by human drivers. For each pair of the predetermined driving parameters, a distribution of the pair of driving parameters is plotted based on their relationship on a two-dimensional (2D) distribution space. The 2D distribution space is partitioned into a number of grid cells, each grid cell representing a particular pair of driving parameters. For each of the grid cells, a probability is calculated that the pair of driving parameter likely falls in the grid cell. A grid table is generated corresponding to the pair of driving parameters. The grid table can be utilized during the autonomous driving at real-time or during simulation to determine a ride stability of an autonomous driving vehicle (ADV) in view of the pair of driving parameters.

Abstract: In one embodiment, a process is performed during controlling Autonomous Driving Vehicle (ADV). A confidence level associated with a sensed obstacle is determined. If the confidence level is below a confidence threshold, and a distance between the ADV and a potential point of contact with the sensed obstacle is below a distance threshold, then performance of a driving decision is delayed. Otherwise, the driving decision is performed to reduce risk of contact with the sensed obstacle.