Automotive Research & Testing Center

Tsung-Ming Hsu

Cheng-Hsien Wang

Bo-Han Lin

Ming-Kuan Ko

Shou-Ti Cheng

Zhi-Hao Zhang

Yu-Rui Chen

Yu-Ting Lin

Hsiang-Chieh Hsu

I-Chun Kuo

Hsiang Chieh Hsu

Ji-fan Yang

Chien-Feng Wu

Hsaing-Chieh Hsu

Hao-Che Chang

A minimum risk decision system for lane-changing includes at least one processor. The processor includes an external risk region calculating module, a system failure judging module, a self-driving starting condition confirming module, and a decision module. The external risk region calculating module calculates an external risk region. The system failure judging module judges whether a system fails. The self-driving starting condition confirming module confirms whether a self-driving starting condition is satisfied. The decision module includes a judging unit, a horizontal vector calculating unit, a drivable space calculating unit, and a decision executing unit. The judging unit judges whether the host vehicle encounters any one of an external risk, a system failure risk and a self-driving starting condition unsatisfied risk. The decision executing unit executes a minimum risk decision as encountering any one of the external risk, the system failure risk and the self-driving starting condition unsatisfied risk.

Minimum risk decision system and method for lane-changing and non-transitory computer readable media

A self-driving vehicle route planning system detects and transforms environment information of a host vehicle into an aerial view including the coordinate information of coordinate points to recognize and mark traffic lane boundaries, traffic lanes, and other vehicles therein, calculates central points of the traffic lanes, and then, calculates a speed of a front vehicle according to its positions and works out a predicted route thereof. If the predicted route is the same as a driving route of the host vehicle, the front vehicle is used as a route reference point to calculate a final route of the host vehicle; if not, the traffic lane boundary is used as a route reference line to calculate the final route. The invention can plan the route merely using a point cloud data, greatly reducing the cost HD map information and storage space.

Route planning system and method of self-driving vehicle

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.

Vehicle platoon following deciding system based on cloud computing and deciding method thereof

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.

Dynamic velocity planning method for autonomous vehicle and system thereof

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.

Driving risk assessment and control decision-making method for autonomous vehicle

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.

Trajectory determination method for a vehicle

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.

Automatic driving method and device able to diagnose decisions

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.

Method of adaptive trajectory generation for a vehicle

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.

Intersection speed deciding method and system thereof

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.

Method for planning a trajectory for a self-driving vehicle

The intelligent driving method applied to a vehicle includes a support vector machine providing step in which the support vector machine is provided. The support vector machine has been trained by a training process. In the training process, a training dataset is provided to the support vector machine. The training dataset is obtained after an original dataset processed by a dimensionality reducing module and a time scaling module. The intelligent driving method includes a dataset processing step in which p features from an environment sensing unit are processed by the dimensionality reducing module and the time scaling module, and the processed dataset will be provided to the support vector machine. The intelligent driving method further includes a deciding step for providing a driving decision for the vehicle according to a classed result of the support vector machine.

Intelligent driving method for passing intersections based on support vector machine and intelligent driving system thereof

An adaptive method for controlling a vehicle speed and an adaptive device for using the same is disclosed. The method controls a vehicle speed of a host vehicle that is driving. A front vehicle drives in front of the host vehicle. There is a vehicle distance between the host vehicle and the front vehicle. Firstly, first accelerations of the host vehicle are retrieved and the vehicle speeds corresponding to the first accelerations and the vehicle distances corresponded thereof are retrieved. When all the first accelerations are not zero, the extreme values of the first accelerations are deleted. Finally, the vehicle speed of the host vehicle is controlled according to all the retrieved accelerations, their corresponding vehicle distances, and the vehicle speeds corresponded thereof.

Growing community of inventors

Changhua County, Taiwan

Tsung-Ming Hsu