Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect.

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Featured Application: This work is specifically applied to the driving decision-making system of autonomous vehicles, allowing autonomous vehicles to run safely under complex urban road environment.Abstract: Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.

Motor vehicle crashes remain a leading cause of life and property loss to society. Autonomous vehicles can mitigate the losses by making appropriate emergency decision, and the crash injury severity prediction model is the basis for autonomous vehicles to make decisions in emergency situations. In this paper, based on the support vector machine (SVM) model and NASS/GES crash data, three SVM crash injury severity prediction models (B-SVM, T-SVM, and BT-SVM) corresponding to braking, turning, and braking + turning respectively are established. The vehicle relative speed (REL_SPEED) and the gross vehicle weight rating (GVWR) are introduced into the impact indicators of the prediction models. Secondly, the ordered logit (OL) and back propagation neural network (BPNN) models are established to validate the accuracy of the SVM models. The results show that the SVM models have the best performance than the other two. Next, the impact of REL_SPEED and GVWR on injury severity is analyzed quantitatively by the sensitivity analysis, the results demonstrate that the increase of REL_SPEED and GVWR will make vehicle crash more serious. Finally, the same crash samples under normal road and environmental conditions are input into B-SVM, T-SVM, and BT-SVM respectively, the output results are compared and analyzed. The results show that with other conditions being the same, as the REL_SPEED increased from the low (0–20 mph) to middle (20–45 mph) and then to the high range (45–75 mph), the best emergency decision with the minimum crash injury severity will gradually transition from braking to turning and then to braking + turning.

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Purpose Adjacent segment degeneration (ASDeg) after anterior cervical discectomy and fusion (ACDF) seriously affects the long-term efficacy of the operation. Therefore, our team has done a lot of research on allograft intervertebral disc transplantation (AIDT) to prove its feasibility and safety. This study will compare the efficacy between AIDT and ACDF in the treatment of cervical spondylosis. Methods All patients who received ACDF or AIDT in our hospital from 2000 to 2016 and followed up for at least 5 years were recruited and divided into ACDF and AIDT groups. The clinical outcomes including functional scores and radiological data of both groups were collected and compared preoperatively and postoperatively at 1 week, 3 months, 6 months, 12 months, 24 months, 60 months and last follow-up. Functional scores included Japanese Orthopedic Association score (JOA), Neck Disability Index (NDI), Visual Analog Scale of Neck (N-VAS) and Arms (A-VAS) pain, the Short Form Health Survey-36 (SF-36) and imaging dates including digital radiographs in the lateral, hyperextension and flexion positions to assess the stability, sagittal balance and mobility of the cervical spine and magnetic resonance imaging (MRI) scans to assess the degeneration of adjacent segment. Results There were 68 patients with 25 in AIDT group and 43 in ACDF group. Satisfactory clinical results were obtained in both groups, but the long-term NDI score and N-VAS score in the AIDT group were better. The AIDT obtained the same stability and sagittal balance of the cervical spine as fusion surgery. The range of motion of adjacent segments can be restored to the preoperative level after transplantation, but this increases significantly after ACDF. There were significant differences in the superior adjacent segment range of motion (SROM) between two groups at 12 months (P = 0.039), 24 months (P = 0.035), 60 months (P = 0.039) and the last follow-up (P = 0.011). The inferior adjacent segment range of motion (IROM) and SROM had a similar trend in the two groups. The ratio value of the greyscale (RVG) of adjacent segments showed a downward trend. At the last follow-up, the RVG decreased more significantly in the ACDF group. At the last follow-up, there was a significant difference in the incidence of ASDeg between the two groups (P = 0.000). And the incidence of adjacent segment disease (ASDis) is 22.86% in the ACDF group. Conclusion The allograft intervertebral disc transplantation may be as an alternative technique to traditional anterior cervical discectomy and fusion for the management of cervical degenerative diseases. For the more, the results showed it would improve cervical kinematics and reduce the incidence of adjacent segment degeneration.

The travel time between bus stops has obvious characteristics of time interval distribution, and the bus is a typical space-time process object, and its operation has a state transition. In order to predict the travel time between bus stations accurately, a support vector machine (SVM) algorithm is proposed based on the measured travel time between bus stations. Through a large number of GPS data in different periods of time for a reasonable classification summary bin selected the appropriate kernel function to verify. The algorithm is verified by the actual operation data of No. 6 bus in Qingdao Economic and technological Development Zone. The results show that the results of support vector machine model operation are basically in agreement with the actual measured data, and the accuracy is relatively high, and it can even be used to predict bus travel time.