As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great ...importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
•A new method to mine driving patterns from drivers’ behavioral signals was proposed.•The most useful combination of segmentation and clustering methods was obtained.•Our extracted driving patterns ...were descriptive in the maneuver level.•Our proposed methodology could help effective online prediction of driving behavior.
Understanding drivers’ behavioral characteristics is critical for the design of decision-making modules in autonomous vehicles (AVs) and advanced driver assistance systems (ADASs). Current relevant studies are mainly based on supervised learning methods which involve extensive human efforts in model development. This paper proposed a framework for automatic descriptive driving pattern extraction from driving sequence data using unsupervised algorithms. Based on the Bayesian multivariate linear regression model, two unsupervised algorithms were utilized to segment driving sequences into fragments. Three extended latent Dirichlet allocation models were applied to cluster the fragments into multiple descriptive driving patterns. The collected driving data from a naturalistic driving experiment was applied to examine the effectiveness of our proposed framework. Results show that the unsupervised segmentation algorithms could help effectively detect the switch characteristics between two continuous driving maneuvers along time, and the clustered patterns could effectively describe the characteristics of each driving maneuver. The proposed unsupervised framework provides an effective and efficient data mining solution to facilitating deep and comprehensive understanding on drivers’ behavioral characteristics, which will benefit the development of AVs and ADASs.
Fuel economy of hybrid vehicles is affected by their powertrain configurations, powertrain parameters, and energy management strategies. It is most beneficial to optimizing all the three factors ...simultaneously. However, when the design search space is large, an exhaustive, optimal control strategy, such as dynamic programming (DP), is too computationally expensive. Hence, a faster optimization method with higher computational efficiency and acceptable accuracy is required. Based on the DP approach, an approximate optimization method, called rapid dynamic programming (Rapid-DP), is developed and discussed in this paper. This method effectively reduces the decision-making time (the time can be reduced by a factor of 700, compared to the DP approach) for optimal control. The optimization processes and results are described and then compared with the original DP and PEARS + methods under two different driving cycles: FTP72 and HWFET. In conjunction with particle swarm optimization (PSO), the rapid-DP is leveraged, for the first time, to optimize key powertrain parameters for power split hybrid electric vehicles. Based on two power-split hybrids: Toyota Prius and Prius++, the joint optimization approach is exploited to examine vehicular fuel savings attributed to synergistic parameters optimization and operating-mode increase. The multi-mode configuration with optimal component parameters is demonstrated to be most fuel-efficient, with 6.56% and 3.15% fuel reductions under FTP72 and HWFET cycles, respectively, with respect to the original Prius 2010.
•Dynamics of power split hybrid electric vehicles (PS-HEVs) are modeled.•Rapid dynamic programming (Rapid-DP) is introduced.•A joint energy management and component-parameter optimization is made.•Fuel economy of power split hybrid electric vehicles (PS-HEVs) is optimized.•Synergy of operating-mode increase and system optimization is examined.
Precision control of clutch pressure is critical in heavy-duty automatic transmission applications in which the fast response of the clutch actuator is required. A conventional clutch actuator system ...with a pressure-reducing valve (PRV) is not applicable in this kind of application due to the fact that a large transient flow and high output power for power-on shift are necessary. In this paper, a pilot-operated PRV is developed for heavy-duty automatic transmission systems. The developed PRV can make the clutch actuator system have a fast response and a high flow capacity simultaneously. The PRV utilizes a three-stage structure with a high-speed proportional solenoid valve (PSV) as the pilot stage to do the tradeoff between the valve response and the flow capacity. First, a linearized input-output dynamic analytical model for the clutch pressure control system is developed based on fluid dynamics. Then, the parameters are identified, and the model is validated by using experimental data. For the validated input-output model, both open- and closed-loop (feedback) pressure control strategies are designed and implemented in a test setup. It infers from the experimental results that the feedback control can lead to excellent control precision. The developed clutch actuator system is applicable for heavy-duty automatic transmissions.
The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, ...Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel horizon.The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.
Platooning of autonomous vehicles has the potential to significantly benefit road traffic. This paper presents a new robust acceleration tracking control of vehicle longitudinal dynamics toward ...platoon-level automation. Based on a multiple-model switching structure, this design divides the large uncertainties of vehicle dynamics into small uncertainties and, accordingly, develops multiple robust controllers for the multiple-model set. The switching control system automatically selects the most appropriate candidate controller into the loop, according to the errors between current vehicle dynamics and multiple models. This technique offers more consistent and approximately linear node dynamics for upper level platoon control, even under relatively large vehicle uncertainties. Simulation comparison with a sliding model controller and a fixed H-infinity controller is conducted for a passenger car to demonstrate the enhanced robustness of the switching control method. The experimental test for the same car is performed for further validation.
For the path-following and safety problem of road vehicles after a tire blowout, a nonlinear coordinated motion controller is proposed in the framework of the triple-step method that potentially ...benefits the engineering-oriented implementation. A control optimization strategy for coordinating both steering and braking is developed to enhance the control performance and deal with the constraints. Since only the primary impacts of the flat tire are taken into account in the vehicle dynamics model used for controller design, an improved robust design procedure is proposed in the framework of input-to-state stability theory to counteract the model uncertainties. The proposed coordinated controller is evaluated together with steering-alone controller and braking-alone controller, and the comparative results for different tire blow-out scenarios are analyzed. The results and analyses clearly show the effectiveness and advantages of the proposed approach.
Objective
This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs).
Background
Takeover ...requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse.
Method
34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator. The relationships and effects between drivers’ physiological responses, takeover-related factors, and trust-change directions during TTs were explored by the combination of an unsupervised learning algorithm and statistical analyses. Furthermore, different typical machine learning methods were applied to establish recognition models of trust-change directions during TTs based on takeover-related factors and physiological parameters.
Result
Combining the change values in the subjective trust rating and monitoring behavior before and after takeover can reliably measure trust-change directions during TTs. The statistical analysis results showed that physiological parameters (i.e., skin conductance and heart rate) during TTs are negatively linked with the trust-change directions. And drivers were more likely to increase trust during TTs when they were in longer TOR lead time, with more takeover frequencies, and dealing with the stationary vehicle scenario. More importantly, the F1-score of the random forest (RF) model is nearly 77.3%.
Conclusion
The features investigated and the RF model developed can identify trust-change directions during TTs accurately.
Application
Those findings can provide additional support for developing trust monitoring systems to mitigate both drivers’ overtrust and undertrust in conditionally AVs.
To improve the accuracy and efficiency of 3D LiDAR mapping, real-time cooperative SLAM has been considered to explore large and complex areas. To merge the individual maps from multiple robots, it is ...crucial to identify the common areas and obtain alternative matches between them. However, data transmission, especially in sparse networks with narrow bandwidth and limited range, is a challenging issue for the above problem. Since the distribution manner is suitable for limited communication, we proposed a common framework of 3D real-time distributed cooperative SLAM to fill the community gap. Assuming that each robot can communicate with others, the presented framework consists of four key modules: place recognition, relative pose estimation, distributed graph optimization, and communication. Meanwhile, we developed a complete real-time distributed cooperative SLAM system, called RDC-SLAM, by integrating state-of-the-art components into the framework. For computation and data transmission efficiency, descriptor-based registration is used instead of the conventional point cloud matching. An intensity-based descriptor is developed to perform the place recognition and obtain the alternative matches, while an eigenvalue-based segment descriptor is applied to further refine the relative pose estimations between these alternative matches. A distributed graph optimization method is utilized to obtain the maximum likelihood of multi-trajectory estimation. A communication protocol is also designed to associate data among robots that are easy to deploy and have low network requirements. The RDC-SLAM is validated by real-world experiments and exhibits superior performance concerning accuracy, computation efficiency, and data efficiency.
The traffic situation at an urban intersection is complicated, due to the numerous internal conflicts and all kinds of traffic accidents. With the objective of improving road safety, the automotive ...industry is moving toward intelligent vehicles. The major challenges are accurately perceiving the road traffic environment, detecting the potential traffic conflicts, and proposing the alternative driving strategies. This paper summarizes the existing researches on traffic conflicts from the perspective of intelligent vehicles. The intelligent vehicles can perceive the surrounding environment, extract road condition information, and detect obstacles for avoiding collisions or mitigating accidents. It expounds that the perception technology of intelligent vehicles can be divided into three main categories, namely, the perception technology, the communication technology, and the fusion of perception-communication technology. At the same time, the existing technical problems are analyzed. Finally, future development trends in this field are discussed.
PDF
