Abstract: A battery pack (1) includes a housing (2) and an array (40) of electrochemical cells (80) disposed in the housing (2). The housing (2) includes a container (3) and a lid (30) that closes an open end of the container (3). The container (3) has a base (4), a sidewall (8) that surrounds the base (4), and a spring plate (110) disposed inside the side wall (8) between the cells (80) and the sidewall (8). The spring plate (110) is free standing within the container (3) and applies a spring force to the cell array (40) that restrains the cells (80) along an axis normal to the surface of the spring plates (110). The lid (30) includes inwardly-protruding pins (50, 60) that further restrain the cells (80) within the housing (2).

Abstract: In an embodiment, a learning-based dynamic modeling method is provided for use with an autonomous driving vehicle. A control module in the ADV can generate current states of the ADV and control commands for a first driving cycle, and send the current states and control commands to a dynamic model implemented using a trained neural network model. Based on the current states and the control commands, the dynamic model generates expected future states for a second driving cycle, during which the control module generates actual future states. The ADV compares the expected future states and the actual future states to generate a comparison result, for use in evaluating one or more of a decision module, a planning module and a control module in the ADV.

Abstract: In one embodiment, a method, apparatus, and system for generating an optimal path for an autonomous driving vehicle (ADV) is disclosed.

Abstract: Methods and systems for multimodal motion planning framework for autonomous driving vehicles are disclosed. In one embodiment, driving environment data of an autonomous vehicle is received, where the environment data includes a route segment. The route segment is segmented into a number of route sub-segments. A specific driving scenario is assigned to each of the route sub-segments, where each specific driving scenario is included in a set of driving scenarios. A first motion planning algorithm is assigned according to a first assigned driving scenario included in the set of driving scenarios. The first motion planning algorithm is invoked to generate a first set of trajectories. The autonomous vehicle is controlled based on the first set of trajectories.

Abstract: A parking system for autonomous driving vehicles optimizes a solution to a parking problem. The ADV detects a parking lot and selects a parking space. The ADV defines constraints for the parking lot, parking space, and kinematic constraints of the ADV, and generates a plurality of potential parking paths to the parking space, taking into account the constraints of the parking lot, parking space, and kinematics of the ADV, but without taking into any obstacles that may be surrounding the ADV. The ADV determines a cost for traversing each of the parking paths. One or more least cost candidate paths are selected from the parking paths, then one or more candidate paths are eliminated based on obstacles surrounding the ADV. Remaining candidates can be analyzed using a quadratic optimization system. A best parking path can be selected from the remaining candidates to navigate the ADV to the parking space.

Abstract: An ADV may determine a predicted path for a moving obstacle. The ADV may determine a predicted area based on the predicted path. The ADV may determine a path for the ADV based on the predicted area. The path for the ADV may avoid the predicted area when determining the path for the ADV.

Abstract: In one embodiment, a system generates a plurality of trajectory candidates for an autonomous driving vehicle (ADV) from a starting point to an end point of a particular driving scenario. The system generates a reference trajectory corresponding to the driving scenario based on a current state of the ADV associated with the starting point and an end state of the ADV associated with the end point, where the reference trajectory is associated with an objective. For each of the trajectory candidates, the system compares the trajectory candidate with the reference trajectory to generate an objective cost representing a similarity between the trajectory candidate and the reference trajectory. The system selects one of the trajectory candidates as a target trajectory for driving the ADV based on objective costs of the trajectory candidates.

Abstract: According to one embodiment, an obstacle is predicted to move from a starting point to an end point based on perception data perceiving a driving environment surrounding an ADV that is driving within a lane. A longitudinal movement trajectory from the starting point to the end point is generated in view of a shape of the lane. A lateral movement trajectory from the starting point to the end point is generated, including optimizing a shape of the lateral movement trajectory using a first polynomial function. The longitudinal movement trajectory and the lateral movement trajectory are then combined to form a final predicted trajectory that predicts how the obstacle is to move. A path is generated to control the ADV to move in view of the predicted trajectory of the obstacle, for example, to avoid the collision with the obstacle.

Abstract: In one embodiment, in response to detecting an obstacle based on a driving environment surrounding an autonomous driving vehicle (ADV), a system projects the obstacle onto a station-time (ST) graph, where the ST graph indicates a location of the obstacle relative to a current location of the ADV at different points in time. The system determines a first set of end points that are not overlapped with the obstacle within the ST graph, wherein each of the end points in the first set represents a possible end condition. The system generates a first set of trajectory candidates between a starting point representing the current location of the ADV and the end points of the first set based on the ST graph. The system selects one of the trajectory candidates in the first set using a predetermined trajectory selection algorithm to control the ADV in view of the obstacle.

Abstract: A first reference line representing a trajectory from a first location to a second location associated with an autonomous driving vehicle (ADV) is received. The first reference line is segmented into a number of reference line segments. For each of the reference line segments, a quintic polynomial function is defined to represent the reference line segment. An objective function is determined based on the quintic polynomial functions of the reference line segments. An optimization is performed on coefficients of the quintic polynomial functions in view of a set of constraints associated with the reference line segments, such that an output of the objective function reaches minimum while the constraints are satisfied. A second reference line is then generated based on the optimized parameters or coefficients of the quintic polynomial functions of the objective function. The second reference line is then utilized to plan and control the ADV.

Abstract: A first reference line representing a trajectory from a first location to a second location associated with an autonomous driving vehicle (ADV) is received. The first reference line is segmented into a number of reference line segments. For each of the reference line segments, a quintic polynomial function is defined to represent the reference line segment. An objective function is determined based on the quintic polynomial functions of the reference line segments. An optimization is performed on coefficients of the quintic polynomial functions in view of a set of constraints associated with the reference line segments, such that an output of the objective function reaches minimum while the constraints are satisfied. A second reference line is then generated based on the optimized parameters or coefficients of the quintic polynomial functions of the objective function. The second reference line is then utilized to plan and control the ADV.

Abstract: A ride-through and recovery method for DC short circuit faults of a hybrid modular multilevel converter based high-voltage direct current transmission (MMC-HVDC) system, the hybrid MMC including multiple full-bridge sub-modules and half-bridge sub-modules, and the method including: 1) detecting whether a DC short circuit fault occurs, and proceeding to step (2) if yes and continuing detecting if no; 2) realizing ride-through of the DC short circuit fault; 3) detecting whether a DC residual voltage increases, and proceeding to step (4) if yes and continuing detecting if no; and 4) realizing DC short circuit fault recovery.

Abstract: A ride-through and recovery method for DC short circuit faults of a hybrid modular multilevel converter based high-voltage direct current transmission (MMC-HVDC) system, the hybrid MMC including multiple full-bridge sub-modules and half-bridge sub-modules, and the method including: 1) detecting whether a DC short circuit fault occurs, and proceeding to step (2) if yes and continuing detecting if no; 2) realizing ride-through of the DC short circuit fault; 3) detecting whether a DC residual voltage increases, and proceeding to step (4) if yes and continuing detecting if no; and 4) realizing DC short circuit fault recovery.