A forward collision warning (FCW) system is important for avoiding rear-end collisions. When the front vehicle slows down or the risk of rear-end collision increases, the FCW system sends a warning. However, if the warning is provided too late, the following vehicle may not have enough time to stop or slow down smoothly. Here, we propose a new FCW system that detects the driving intention of the front vehicle to provide earlier warning than previously used systems. The proposed FCW system consists of three steps. First, the driving intention of the front vehicle is determined by the driving intention recognition module. Second, the driving intention and other driving parameters of the front vehicle are transmitted to the following vehicle using vehicle-to-vehicle (V2V) communication. Finally, this information and the driving parameters of the following vehicle are used to determine the potential collision risk by the FCW module located in the second vehicle. To evaluate the proposed system, we conducted a simulation test based on PreScan (commercial software provided by TASS international) and actual road tests in various driving scenarios. The simulation test results demonstrated that the correct warning rate of the proposed system was 97.67%, which was 6.34% higher than that of the system with a fixed time-to-collision (TTC) threshold. The real vehicle test results showed that the proposed system was able to provide earlier warnings than the TTC-based system. The timely warning rate, i.e., the ratio of the number of warnings at the beginning of braking to the total number of warnings was 93.33%. The proposed system proved effective for providing early warning to the following vehicle under different driving conditions of the front vehicle.

Ultrasound sound-speed tomography (USST) has shown great prospects for breast cancer diagnosis due to its advantages of nonradiation, low cost, 3-D breast images, and quantitative indicators. However, the reconstruction quality of USST is highly dependent on the first-arrival picking of the transmission wave. Traditional first-arrival picking methods have low accuracy and noise robustness. To improve the accuracy and robustness, we introduced a self-attention mechanism into the bidirectional long short-term memory (BLSTM) network and proposed the self-attention BLSTM (SAT-BLSTM) network. The proposed method predicts the probability of the first-arrival time and selects the time with maximum probability. A numerical simulation and prototype experiment were conducted. In the numerical simulation, the proposed SAT-BLSTM showed the best results. For signal-to-noise ratios (SNRs) of 50, 30, and 15 dB, the mean absolute errors (MAEs) were 48, 49, and 76 ns, respectively. The BLSTM had the second-best results, with MAEs of 55, 56, and 85 ns, respectively. The MAEs of the Akaike information criterion (AIC) method were 57, 296, and 489 ns, respectively. In the prototype experiment, the MAEs of the SAT-BLSTM, the BLSTM, and the AIC were 94, 111, and 410 ns, respectively.

Driver’s intention of the front vehicle plays an important role in the automatic emergency braking (AEB) system. If the front vehicle brakes suddenly, there is potential collision risk for following vehicle. Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. According to the recognized driver’s intention transmitted by Internet of vehicles, an AEB model for the following vehicle is proposed, which can dynamically change the critical braking distance under different driving conditions to avoid rear-end collision. In order to verify the performance of the proposed models, we conducted driver’s intention recognition and AEB simulation tests in the cosimulation environment of Simulink and PreScan. The simulation test results show that the average recognition accuracy of the proposed BP-HMM model was 98%, which was better than that of the BP and HMM models. In the Car to Car Rear moving (CCRm) and Car to Car Rear braking (CCRb) tests, the minimum relative distance between the following vehicle and the front vehicle was within the range of 1.5 m–2.7 m and 2.63 m–5.28 m, respectively. The proposed AEB model has better collision avoidance performance than the traditional AEB model and can adapt to individual drivers.

High detection accuracy in piezoelectric-based force sensing in interactive displays has gained global attention. To achieve this, artificial neural networks (ANN)—successful and widely used machine learning algorithms—have been demonstrated to be potentially powerful tools, providing acceptable location detection accuracy of 95.2% and force level recognition of 93.3% in a previous study. While these values might be acceptable for conventional operations, e.g., opening a folder, they must be boosted for applications where intensive operations are performed. Furthermore, the relatively high computational cost reported prevents the popularity of ANN-based techniques in conventional artificial intelligence (AI) chip-free end-terminals. In this article, an ANN is designed and optimized for piezoelectric-based touch panels in interactive displays for the first time. The presented technique experimentally allows a conventional smart device to work smoothly with a high detection accuracy of above 97% for both location and force level detection with a low computational cost, thereby advancing the user experience, and serviced by piezoelectric-based touch interfaces in displays.

Bent-ray reconstruction method can improve sound speed image quality, compared with straight-ray reconstruction. However, it suffers from time-consuming ray-linking iteration, because multiple rays with different launch angles have to be iteratively traced to find the ray linking the given emitter and receiver. In this study, a novel virtual receiver bent-ray method (VRBRM) was proposed to avoid the ray-linking iteration. In this method, the angle of the straight line connecting a given emitter and receiver is calculated as the launch angle, and only one ray with the calculated launch angle is traced and its arrival position at the ring transducer is recorded as the virtual receiver position. Then, the sound propagation time from the given emitter to the virtual receiver is interpolated. The results of simulation, phantom and ex vivo experiments demonstrate that the proposed method can reduce the reconstruction time to about 30% without reduction of the reconstruction quality.