Abstract: A semiconductor device includes an epitaxial substrate. The epitaxial substrate includes a substrate. A strain relaxed layer covers and contacts the substrate. A III-V compound stacked layer covers and contacts the strain relaxed layer. The III-V compound stacked layer is a multilayer epitaxial structure formed by aluminum nitride, aluminum gallium nitride or a combination of aluminum nitride and aluminum gallium nitride.

Abstract: A package structure including a photonic, an electronic die, an encapsulant and a waveguide is provided. The photonic die includes an optical coupler. The electronic die is electrically coupled to the photonic die. The encapsulant laterally encapsulates the photonic die and the electronic die. The waveguide is disposed over the encapsulant and includes an upper surface facing away from the encapsulant. The waveguide includes a first end portion and a second end portion, the first end portion is optically coupled to the optical coupler, and the second end portion has a groove on the upper surface.

Abstract: A package includes a photonic layer on a substrate, the photonic layer including a silicon waveguide coupled to a grating coupler; an interconnect structure over the photonic layer; an electronic die and a first dielectric layer over the interconnect structure, where the electronic die is connected to the interconnect structure; a first substrate bonded to the electronic die and the first dielectric layer; a socket attached to a top surface of the first substrate; and a fiber holder coupled to the first substrate through the socket, where the fiber holder includes a prism that re-orients an optical path of an optical signal.

Abstract: A semiconductor package includes a first die stack structure and a second die stack structure, an insulating encapsulation, a redistribution structure, at least one prism structure and at least one reflector. The first die stack structure and the second die stack structure are laterally spaced apart from each other along a first direction, and each of the first die stack structure and the second die stack structure comprises an electronic die; and a photonic die electronically communicating with the electronic die. The insulating encapsulation laterally encapsulates the first die stack structure and the second die stack structure. The redistribution structure is disposed on the first die stack structure, the second die stack structure and the insulating encapsulation, and electrically connected to the first die stack structure and the second die stack structure. The at least one prism structure is disposed within the redistribution structure and optically coupled to the photonic die.

Abstract: A vehicle platoon following deciding system based on cloud computing is configured to decide a plurality of vehicle platoon accelerations of a leading vehicle and at least one following vehicle. A cloud processing unit receives a leading vehicle parameter group and at least one following vehicle parameter group. The cloud processing unit is configured to implement a cloud deciding step. The cloud deciding step includes judging whether the leading vehicle is manually driven according to the leading vehicle parameter group to generate a driving mode judging result, calculating a driving acceleration range according to a leading vehicle acceleration range and at least one following vehicle acceleration range, estimating a compensated acceleration according to the leading vehicle parameter group, and calculating the vehicle platoon accelerations according to the driving mode judging result and at least one of the driving acceleration range and the compensated acceleration.

Abstract: An allochronic obstacle avoidance system for platooning is configured to decide an obstacle avoidance of a leading vehicle and at least one following vehicle. A sensing device is configured to generate an obstacle position and an obstacle speed. A leading vehicle processing unit is configured to transmit a leading vehicle parameter group. At least one following vehicle processing unit is configured to transmit at least one following vehicle parameter group. A cloud processing unit is configured to implement a cloud deciding step including predicting a leading vehicle free space and at least one following vehicle free space according to the leading vehicle parameter group and the at least one following vehicle parameter group, and deciding the obstacle avoidance of the leading vehicle and the at least one following vehicle according to the leading vehicle free space and the at least one following vehicle free space.

Abstract: A vehicle platoon following deciding system based on cloud computing is configured to decide a plurality of vehicle platoon accelerations of a leading vehicle and at least one following vehicle. A cloud processing unit receives a leading vehicle parameter group and at least one following vehicle parameter group. The cloud processing unit is configured to implement a cloud deciding step. The cloud deciding step includes judging whether the leading vehicle is manually driven according to the leading vehicle parameter group to generate a driving mode judging result, calculating a driving acceleration range according to a leading vehicle acceleration range and at least one following vehicle acceleration range, estimating a compensated acceleration according to the leading vehicle parameter group, and calculating the vehicle platoon accelerations according to the driving mode judging result and at least one of the driving acceleration range and the compensated acceleration.

Abstract: A dynamic velocity planning method for an autonomous vehicle is performed to plan a best velocity curve of the autonomous vehicle. An information storing step is performed to store an obstacle information, a road information and a vehicle information. An acceleration limit calculating step is performed to calculate the vehicle information according to a calculating procedure to generate an acceleration limit value range. An acceleration combination generating step is performed to generate a plurality of acceleration combinations according to the obstacle information, the road information, and the acceleration limit value range. An acceleration filtering step is performed to filter the acceleration combinations according to a jerk threshold and a jerk switching frequency threshold to obtain a selected acceleration combination. An acceleration smoothing step is performed to execute a driving behavior procedure to adjust the selected acceleration combination to generate the best velocity curve.

Abstract: A driving risk assessment and control decision-making method for an autonomous vehicle includes: detecting the surrounding state of the vehicle multiple times to generate multiple sensing signals; quantifying the sensing signals to generate multiple sensing values and calculating a sensing average value of the sensing values; calculating a sensing error value between each sensing value and the sensing average value, a sensing error average value of sensing error values and a sensing error variation value; integrating the sensing error average value, the sensing error variation value and a sensor systematic error average value and a sensor systematic error variation value to generate a sensing signal correction value; combining the sensing values and the sensing signal correction value to generate multiple sensing signal reference values; judging whether a stability of the sensing signal reference values falls within a preset range; generating a control mechanism based on the judgement.

Abstract: A trajectory determination method for a vehicle is provided. A target vehicle trajectory is determined from among multiple candidate vehicle trajectories by considering, for each of the candidate vehicle trajectories, presence or absence of a front obstacle, presence or absence of a potentially-colliding obstacle, and a condition related to lane change, so as to enhance driving safety of the vehicle.

Abstract: A trajectory determination method for a vehicle is provided. A target vehicle trajectory is determined from among multiple candidate vehicle trajectories by considering, for each of the candidate vehicle trajectories, presence or absence of a front obstacle, presence or absence of a potentially-colliding obstacle, and a condition related to lane change, so as to enhance driving safety of the vehicle.

Abstract: A dynamic velocity planning method for an autonomous vehicle is performed to plan a best velocity curve of the autonomous vehicle. An information storing step is performed to store an obstacle information, a road information and a vehicle information. An acceleration limit calculating step is performed to calculate the vehicle information according to a calculating procedure to generate an acceleration limit value range. An acceleration combination generating step is performed to generate a plurality of acceleration combinations according to the obstacle information, the road information, and the acceleration limit value range. An acceleration filtering step is performed to filter the acceleration combinations according to a jerk threshold and a jerk switching frequency threshold to obtain a selected acceleration combination. An acceleration smoothing step is performed to execute a driving behavior procedure to adjust the selected acceleration combination to generate the best velocity curve.

Abstract: A driving risk assessment and control decision-making method for an autonomous vehicle includes: detecting the surrounding state of the vehicle multiple times to generate multiple sensing signals; quantifying the sensing signals to generate multiple sensing values and calculating a sensing average value of the sensing values; calculating a sensing error value between each sensing value and the sensing average value, a sensing error average value of sensing error values and a sensing error variation value; integrating the sensing error average value, the sensing error variation value and a sensor systematic error average value and a sensor systematic error variation value to generate a sensing signal correction value; combining the sensing values and the sensing signal correction value to generate multiple sensing signal reference values; judging whether a stability of the sensing signal reference values falls within a preset range; generating a control mechanism based on the judgement.

Abstract: A hybrid planning method in an autonomous vehicle is performed to plan a best trajectory function of a host vehicle. A parameter obtaining step is performed to sense a surrounding scenario of the host vehicle to obtain a parameter group to be learned. A learning-based scenario deciding step is performed to receive the parameter group to be learned and decide one of a plurality of scenario categories that matches the surrounding scenario of the host vehicle according to the parameter group to be learned and a learning-based model. A learning-based parameter optimizing step is performed to execute the learning-based model with the parameter group to be learned to generate a key parameter group. A rule-based trajectory planning step is performed to execute a rule-based model with the one of the scenario categories and the key parameter group to plan the best trajectory function.

Abstract: An automatic driving method and device able to diagnose decisions is disclosed herein, wherein a vehicle body signal sensor detects vehicle body information, and an environment sensor detects traffic environment information. The information is transmitted to a central processor to generate a future driving track. The central processor examines whether the differences between the future driving track and the traffic environment information and the indexes of the future driving track meet tolerances. If no, the central processor transmits notification information to an automatic driving controller. If yes, the central processor transmits the future driving track to the automatic driving controller to make the automatic driving controller undertake automatic driving according to the future driving track. The present invention can automatically judge whether the future driving track generated by the central processor is within tolerances and determine whether the automatic driving track is safe.

Abstract: A method of adaptive trajectory generation for a vehicle is provided. A computer device of the vehicle may update a current trajectory for the vehicle when some predetermined conditions that are related to an obstacle positioned within a predetermined distance of the vehicle are satisfied.

Abstract: An intersection speed deciding method includes a dataset obtaining and calculating step and a speed adjusting step. At least one of the vehicles expected to pass through one of the points is defined as the approaching vehicle, and a first arrival time of the approaching vehicle is obtained. Whether a preceding vehicle is on the host route is judged. If yes, a second arrival time of the preceding vehicle is obtained. Whether the host vehicle is expected to wait for a red light is judged. If yes, a red light duration is obtained. A time difference exists between a best arrival time and a corresponding expected arrival time, and each time difference is minimized based on the first arrival time, the second arrival time and the red time duration. A speed of the host vehicle is adjusted or remained based on the expected arrival times.

Abstract: A method for planning a trajectory for a self-driving vehicle on a road includes: generating multiple target planned trajectory sets based on information concerning the self-driving vehicle; calculating multiple projected moving ranges and multiple projected moving speeds of an obstacle based on information concerning the obstacle, the projected moving ranges corresponding respectively with multiple time points in a driving time period, the projected moving speeds corresponding respectively with the time points; and selecting one of the target planned trajectory sets as an optimal planned trajectory set based on the target planned trajectory sets, the projected moving ranges and the projected moving speeds for the obstacle.

Abstract: A method of adaptive trajectory generation for a vehicle is provided. A computer device of the vehicle may update a current trajectory for the vehicle when some predetermined conditions that are related to an obstacle positioned within a predetermined distance of the vehicle are satisfied.

Abstract: An intersection speed deciding method includes a dataset obtaining and calculating step and a speed adjusting step. At least one of the vehicles expected to pass through one of the points is defined as the approaching vehicle, and a first arrival time of the approaching vehicle is obtained. Whether a preceding vehicle is on the host route is judged. If yes, a second arrival time of the preceding vehicle is obtained. Whether the host vehicle is expected to wait for a red light is judged. If yes, a red light duration is obtained. A time difference exists between a best arrival time and a corresponding expected arrival time, and each time difference is minimized based on the first arrival time, the second arrival time and the red time duration. A speed of the host vehicle is adjusted or remained based on the expected arrival times.