Slip parameter prediction is crucial for motion planning and control of unmanned skid-steering vehicles in off-road environments. Slip parameter prediction methods based on nonlinear observers and those based on machine learning models both have limitations under various conditions. Therefore, this paper presents a multi-layer Adaptive Unscented Kalman Filter (AUKF) slip parameter prediction method based on a fusion framework of nonlinear observers and machine learning models. The method first constructs the 0-layer AUKF using the vehicle kinematic model and sensor data to initialize the slip parameters. Then, with the input of the desired sequences of wheel speeds generated by the autonomous driving system, the 1-layer AUKF is constructed by combining the machine learning predictive model and running N times to obtain the future slip parameter sequence. Experimental data was collected by driving on paved and dirt roads with a skid-steering vehicle. The experimental results show that the method in this paper outperforms methods based on nonlinear observers in terms of slip parameter prediction accuracy when the prediction time domain is long. Furthermore, when faced with unknown conditions, this method shows superior robustness compared to methods based on machine learning models.

Intelligent Connected Vehicles (ICVs), have developed rapidly towards information networking, and they are destined to become an important carrier of future travel. However, they also face some challenges like cyber-security. Controller Area Network (CAN), as a typical communication medium of ICVs, is very vulnerable to cyber-attacks since they lack identity authentication and message encryption. Therefore, how to pinpoint the source of abnormal messages, also namely Electronic Control Unit (ECU) source mapping scheme, is of significance to the in-vehicle communication. However, most existing studies that utilize learning algorithms consider little on the stability of training features. To this end, this paper develops a hybrid-feature-extraction based ECU mapping scheme derived from physical features of network signals. In detail, more stable bit-block groups and mode value of high-level voltages are extracted as training features. On this basis, single-layer Softmax classifier is formulated to accurately identify ECU sources. Finally, the proposed scheme is experimentally validated with eight ECUs in a real-world vehicle. Results show that the average identification accuracy of all ECUs is close to 98%, which has been improved by more than 6% compared with other typical methods. The present study effectively addresses the deficiency of identity authentication in CAN.

This paper develops a learning model for personalized adaptive cruise control that can learn from human demonstration online and mimic a human driver's driving strategies in the dynamic traffic environment. Under the framework of the proposed model, reinforcement learning is used to capture the human-desired driving strategy, and the proportion-integration-differentiation controller is adopted to convert the learning strategy to low-level control commands. The performance of the learning model is tested in the simulation environment built in a driving simulator using PreScan. Experimental results show that the learning model can duplicate human driving strategies with acceptable errors. Moreover, compared with the traditional adaptive cruise control, the proposed model can provide better driving comfort and smoothness in the dynamic situation.

The 2011 Intelligent Vehicle Future Challenge (11'IVFC) tested self-driving systems in real urban scenarios. The entry of Beijing Institute of Technology: BIT-III finished the 10-kilometer long track in 28 minutes without human operation and obeyed traffic regulations in most circumstances. This paper presented the design and implementation of BIT-III. As a universal self-driving system, BIT-III valued extensibility and featured modularized system architecture. For a better compatibility with diverse sensing devices, BIT-III classified perception to be either OGM (Occupancy Grid Map)-oriented or object-oriented based on the output mode. To work in environments with uncertainties, BIT-III gave first priority to safety and stability in driving, and realized them in the core-level components as the instinct of the system. Even in the unknown environment in the ll'IVFC, BIT-III was able to drive smoothly without crashes.

UNLABELLED: High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species. AVAILABILITY: miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php.