In the process of autonomous development, potential defects should be discovered and solved to ensure the Safety of the Intended Functionality (SOTIF), and the scenario-based test is currently the mainstream approach of testing autonomous. However, most existing scenario generation methods either reduce the behavioral details of traffic participants in parameterization, or take expensive time consumption, especially when generating the scenarios involving multiple traffic participants in real-time tests. In this paper, we propose a generation framework involving a cost-based controller and a propagating knowledge library, which can generate hazardous multi-participants scenarios and form sectionalized complex behavior to reflect diverse failure forms. We carry out a real-time test with the system under test (SUT) in the loop to verify our proposed framework, and the results show that our framework can generate expected scenario cases for a black-box target within several minutes, and the generation results of failure forms are better-distributed compared to parameterizing methods.

During the transition from human-driving to autonomous-driving, Automated Driving Systems (ADS) have to deal with the crises caused by uncertain human-driving factors, especially in the junction-scenes which are flexible and complex. To validate the Safety of the Intended Functionality (SOTIF) in ADS with scenario-based tests, it is challenging to create reasonable hazardous-test-cases, which consider causing collision and avoiding liability simultaneously. Currently, parameter-search-based approaches with parameterized-maneuvers are widely utilized to explore test cases. However, in complicated scenes (i.e., junction-scenes), the trade-off between efficiency and fineness of maneuver-parameterization reduces the searching ability. To address the above challenges, we propose a cost-based controller with a designed state-transfer for collision to lead the agent vehicle (i.e., the vehicle causing events). The approach directly outputs continuous actions as the substitute for discrete parameterized-maneuvers, reducing the searching-state-space and enabling more detailed behaviors. In our real-time test, given different scenes in which Ego owns the right-of-way, our approach generates Ego-liable collision-cases with the success rates above 90%, higher than the ones using parameter-search-based method which are below 70%. More importantly, our method creates more hazardous cases with higher efficiency, which achieves 1.9-2.6 times of the impact-speed and takes only 18%-24% of time-consumption by contrast.

Collision risk modeling with multiple surrounding target vehicles (TVs) is essential for host vehicle (HV) trajectory planning, especially considering challenging TV lateral behaviors. Existing motion-compensated spatial methods ignore TV lateral motion states such as lateral velocity and yaw rate, so that TV lateral behavior cannot be described accurately. Aiming at high-accuracy collision risk modeling, a twisted Gaussian risk model using both longitudinal and lateral motion states for TV behavior description is proposed. Firstly, the HV-TVs system is treated as the superposition of multiple HV-TV units, and a Gaussian risk model is adopted for the collision risk description of the HV-TV unit. Then, by expanding the variances, TV longitudinal and lateral velocities are considered. At last, a twisted Gaussian risk model considering TV yaw rate is constructed based on the projection of the Gaussian risk model. With this twisted Gaussian risk model, TV longitudinal-lateral motion states are considered simultaneously, and TV behaviors can be described for HV-TVs collision risk modeling. For HV trajectory planning, trajectory candidates generated by the maneuver-inspired method are evaluated via the proposed risk model, and the safe and efficient trajectory is selected. Simulation and hardware-in-the-loop experimental results show that the proposed method considering TV longitudinal-lateral motion states allows HV to operate more safely and efficiently than the conventional method.

The stochastic geometry-based modeling and analysis of large-scale mobile edge computing (MEC) networks are vital for the effective configuration of MEC networks. In this paper, we develop a meta-analytical framework for MEC-enabled heterogeneous networks with the communication-computation-aware (CCA) user association mechanism. Compared with the communication-based user association mechanisms in most existing works, the CCA user association mechanism can capture the impacts of network computation capability on the association process between the user and MEC access point, at the expense of dealing with the more complex coupling of communication and computing. Given the need for interference characterization, we first derive the essential prerequisite quantities (i.e., per-tier association probability, link distance distribution, interferer process intensity, etc.) to represent the computation-dependent interference model. Further, the moment and the meta distribution of the task success offloading probability are derived, based on which we investigate the task execution latency performance, including the communication latency, local computing latency, and edge computing latency. By theoretical analysis and simulation results, it is demonstrated that the proposed analytical framework can provide accurate fine-grained network information for the MEC-enabled HetNets. Moreover, we elaborate on the impacts of the edge computation capability on the network performance and reveal important tradeoffs of the performance metrics.

Integrated sensing and communication (ISAC) technique has recently gained significant research interest by supporting dual functions by a unified hardware platform. Traditionally, the ISAC system deemed the sensing and communication functions restricted by each other. In this paper, we demonstrate that a mutual benefit between sensing and communication functions can be achieved for the millimeter-wave ISAC system by efficiently exploiting the common information shared by both functions. Specifically, the sensing estimation can potentially be utilized to improve beamforming accuracy and the communication echo signal can enhance the sensing estimation as well. To demonstrate the mutual performance gain, we evaluate the joint outer bounds of the communication and sensing estimation rates. First, we derive the ergodic communication rate when the communication beamforming is assisted by sensing estimation. The asymptotic characteristic is further explored when the number of antennas approaches to infinity. Then, the closed-form estimation rate is obtained in terms of the Cramér-Rao Bound when the sensing estimation is assisted by communication signals. Numerical results verify the superiority of the mutual benefit when the common information was efficiently exploited by the ISAC BS transceiver. And when the transmission power is equally allocated, there are 10% and 2% gains on the communication rate and the sensing estimation rate simultaneously.

Accurate trajectory prediction of surrounding vehicles is vital for automated vehicles to achieve high-level driving safety in complex situations. However, most state-of-the-art approaches for multi-vehicle trajectory prediction ignore vehicle motion uncertainty caused by different driving styles. Moreover, the interrelationship between the vehicle and the environment is seldom considered. To address the above problems, this paper proposes a driving risk map-integrated deep learning (DRM-DL) method for interactive trajectory prediction of surrounding vehicles, which comprehensively considers the motion uncertainty, trajectory intention uncertainty and interactions among vehicles, lane lines and road boundaries. Specifically, we adopt a conditional variational autoencoder (CVAE) to generate the candidate trajectories, in which the motion uncertainty is considered using a conditional Gaussian distribution. Furthermore, a driving risk map is constructed to realize a unified and interpretable representation of vehicle-vehicle and vehicle-environment interactions. The probability of each candidate trajectory is assigned using a trajectory probability model and a random selection is adopted to select a guided trajectory, which simulates the driver’s trajectory intention uncertainty. Finally, a relearning module is designed to obtain the precise trajectory prediction for surrounding vehicles. The proposed method is evaluated on the HighD dataset, and the results demonstrate a more accurate and reliable trajectory prediction for surrounding vehicles compared with state-of-the-art methods.

A timely understanding of target vehicles (TVs) lateral behavior is essential for the decision-making and control of host vehicle. Existing physical model-based methods such as motion-based method and multiple centerline-based method are generally constructed based on TV pose and longitudinal velocity, and tend to ignore TV preview driving characteristic and other useful information such as lateral velocity and yaw rate. To address these issues, a driver preview and multiple centerline model-based probabilistic behavior recognition architecture is proposed for timely and accurate TV lateral behavior prediction. Firstly, a driver preview model is used to describe vehicle preview driving characteristic, and TV preview lateral offset and preview lateral velocity are calculated with TV states and road reference information. Then, the preview lateral offset and preview lateral velocity are combined with multiple centerline model for TV lateral behavior reasoning based on the interacting multiple model-based probabilistic behavior recognition algorithm. With this method, TV preview driving characteristic and lateral motion states are combined for precise TV lateral behavior description. Furthermore, to predict short-term lateral behavior, a preview lateral velocity-dependent transition probability matrix model constructed with Gaussian cumulative distribution function is proposed. Simulation and experimental results show that the proposed method considering vehicle preview driving characteristic predicts TV lateral behavior earlier than the conventional method.

Preceding target vehicle (PTV) motion recognition play a pivotal role in autonomous vehicles. Motion states such as yaw rate, longitudinal and lateral velocity are critical for ego vehicle decision-making and control. However, lateral states of a PTV can hardly be measured directly by common onboard sensors and the PTV lateral state estimation has been seldom addressed in existing literatures. In this paper, a novel estimation scheme based on multiple neural network ensemble is proposed for PTV lateral state estimation. First, PTV lateral kinematics is presented based on vehicle-road relationship and a novel PTV lateral motion model is constructed to interpret the PTV lateral motion. Then, neural network observer with the PTV lateral kinematics as prior knowledge is designed and training data are collected in simulation environment. The neural network observer is trained using Levenberg-Marquardt backpropagation with Bayesian regularization (LMBR) to improve the generalization capability. Finally, to further improve the performance of the neural network estimation method, multiple neural network observers are integrated by weighted averaging strategy. The effectiveness of proposed approach is verified through hardware-in-the-Ioop (HiL) experiments conducted in designed verification scenarios, and compared with model-based method and other three learning methods. The experiment results reveal that the proposed method outperforms other typical methods and achieves accurate estimation of the PTV lateral states.

Automated vehicle control requires full knowledge of motion behavior of the preceding target vehicles (PTVs), and the states such as longitudinal/lateral velocity and yaw rate are critical for the PTV behavior description. However, the PTV's lateral states estimation have seldom been addressed in the state-of-the-art literatures. Aimed at providing reliable PTV lateral states, this paper presents a novel combined model-based estimation scheme. Different from the conventional PTV models, the proposed model is constructed based on the host-target vehicle dynamics and road constraints. Specifically, steering angle of the PTV is included in the state vector. The measurements, such as heading angle, road curvature, and lateral distance to the lane center, are available from an onboard vision system. As a vision system inevitably has measurement delay, a modified Kalman filter is developed to address the sampling issue. To verify the proposed approach, hardware-in-the-loop experiments are conducted in designed testing scenarios.

In this paper, a novel model reference adaptive (MRA) based speed and rotor position estimation strategy is proposed for the surface mounted permanent magnet synchronous motor (SPMSM). In the proposed strategy, a new adaptive law is proposed by considering the MRA adaptive law as a controller. Furthermore a parameter self-turning strategy and a speed compensation strategy are proposed to improve the performance of the MRA method. Finally simulation results, which are obtained by the MATLAB/Simulink software, are presented to verify the feasibility and effectiveness of the proposed strategy.