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In the development of automated driving vehicle, it is important to detect the driving intentions of the remote vehicles, such as if the remote vehicle on left lane intends to keep driving along the same lane (lane keeping) or change to right lane (change to right lane which results in cut in to host lane), or if the lead vehicle intends to follow the vehicle on the adjacent lane and then change to that adjacent lane. In this paper, we have proposed and implemented a remote vehicle driving intention estimation system which specifically detects the driving intentions of remote vehicles in lateral direction. The estimation FOV covers the three lanes (left, ego, right). The main estimated driving intentions include lane keeping, start lane change to left or right side, arriving to left or ego or right lane, etc.

This paper first presents a basic mean value engine plant model implemented in a hardware-in-the-loop (HIL) system. The plant model includes some basic engine parameters such as engine speed, manifold absolute pressure, etc., which are critical to both control algorithm integrity and default actions that result from improper signal performance (e.g., ECU shuts down due to corrupted signal(s)). The model is then improved to develop the HIL bench-based testing capabilities in the areas where a vehicle has traditionally been required. The on-board diagnostic monitor tests covered by SID $06 of SAE J1979 are selected as a case study. Specifically, for OBD exhaust gas sensor monitor testing purposes, the oxygen sensor model is developed to simulate normal or abnormal binary switching signals which might have asymmetric “lean to rich” and “rich to lean” transitions, or largely off maximum/minimum sensor voltages, etc.

Most times in ECU system function testing, the sensor input signals are directly set to a known value in order to drive the corresponding software variable to within a range of an expected value. This works only if the transfer function from the physical signal input to the software variable is well defined such as the measurement on MAP, A/C pressure, etc. Nevertheless, there are times the transfer function is not clearly defined and it is difficult to drive the software variable to an expected value. One example is throttle position sensor (TPS) test in an electronic throttle control (ETC) system, where TPS is not directly driven by the driver accelerator pedal sensor (APS) and it is very difficult to get TPS to an expected range by only changing APS. This paper introduces a method to use feedback in an HIL based ECU testing system to control outputs to an expected range. In this case study, the signal to be controlled is connected back to the HIL system to provide feedback.

In a CAS system, the distance and relative velocity between front and host vehicles are estimated to calculate time-to-collision (TTC). The distance estimates by different methods will certainly include noise which should be removed to ensure the accuracy of TTC calculations. Kalman filter is a good tool to filter such type of noise. Nevertheless, Kalman filter is a model based filter, which means a correct model is important to get the good filtering results. Usually, a vehicle is either moving with a constant velocity (CV) or constant acceleration (CA) maneuvers. This means the distance data between front and host vehicles can be described by either constant velocity or constant acceleration model. In this paper, first, CV and CA models are used to design two Kalman filters and an interacting multiple model (IMM) is used to dynamically combine the outputs from two filters.

To confirm the correct operation and detect the potential errors in the ever more complicated automotive ECU application software are very challenging. This paper presents a new approach to detect potential ECU application software abnormal behavior based on the Mahalanobis Distance, the Mahalanobis-Taguchi System, and vehicle driving data playback capability with a simulator. Vehicle driving data is recorded by instrumentation calibration tools and played back on the test bench to stimulate the ECU. In our study, the normal behavior is characterized by the Mahalanobis Distance (MD), which is calculated from the data set logged while playing back the recorded vehicle maneuvers while the ECU is flashed with “baseline software” that was believed to be error free. Then the MDs were calculated from a new data set logged while playing back the same vehicle maneuvers while the ECU was flashed with the new software.