Automotive Research & Testing Center

Cheng-Hsien Wang

Tsung-Ming Hsu

Yu-Rui Chen

Hsien-Chang Chiu

Hsiang-Chieh Hsu

A magnet-guiding switching system includes a lane magnetic field device, a switching controlling module and a route controlling center. As the switching controlling unit informs the magnetic field switching unit to turn on the first switching electromagnets and to turn off the second switching electromagnets according to the driving signal, the vehicle is guided by magnetic fields of the first switching electromagnets to drive from the first section of the first route to the second section along the guiding section; as the switching controlling unit informs the magnetic field switching unit to turn on the second switching electromagnets and to turn off the first switching electromagnets according to the driving signal, the vehicle is guided by magnetic fields of the second switching electromagnets to drive from the first section of the first route to the continuous section of the second route along the switching section.

Magnet-guiding switching system and magnet-guiding switching method

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

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

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

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.

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Changhua County, Taiwan

Cheng-Hsien Wang