Improper handling of on-ramp merging may cause severe decrease of traffic efficiency and contribute to lower fuel economy, even increasing the collision risk. Cooperative control for connected and automated vehicles (CAVs) has the potential to significantly reduce the negative impact and improve safety and traffic efficiency. Implementation of cooperative on-ramp merging requires the assistance of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, wherein the communication delay may cause negative impact on CAV cooperative control. In this paper, scenario of on-ramp merging for CAVs considering the V2I communication delay are studied. Statistical characteristics of the V2I communication delay are explored from both literature and real field test, and a communication delay estimation model based on statistical techniques are proposed. Specifically, we firstly model the CAV on-ramp merging scenario using optimal control in ideal situation. Then, several statistical characteristics of the V2I communication are investigated especially the probability density function of the V2I communication delay in several application scenarios. Further, we proposed a communication delay estimation model and used the modified vehicle state to compute the corresponding control law. Real field test of V2I communication delay indicated that distribution of V2I communication delay could correlate with the application scenario and normal distribution can be generally adopted to approximate the probability density function (PDF) when the number of samples is large enough. Numerical simulation of the CAV on-ramp merging scenario considering the V2I communication delay revealed that dynamic performance of the control process would be deteriorated impacted by the V2I communication delay and it might further impact the final control effect and lead to potential lateral collision in the merging area.

To guarantee the safety and reliability of autonomous driving applications, it is indispensable to construct a proper fault diagnosis framework tailored to autonomous vehicles. Fault diagnosis aims to provide essential information about the system operational status and its interpretation facilitates decision-making and mitigates potential operation risks. In the present work, interpretability issue in fault diagnosis for autonomous vehicles is discussed from the sensor data analytics perspective. Environmental impact is first evaluated using the noise energy as a measure to interpret the impact on sensor data caused by the environment. A signal quality indicator is proposed and Savitzky-Golay filters are applied for online denoising, acting as a countermeasure to mitigate the impact and to enhance data quality. Then, the adversarial learned denoising shrinkage autoencoder (ALDSAE), an adversarial learning neural network, is constructed for sensor data anomaly detection. It considers an adversarial training technique to improve the performance of the anomaly detector. A residual explainer specific to the ALDSAE model is employed to calculate the contribution of each input feature to the anomaly score in order to interpret the anomaly detection results. Several experiments with the collected data from an autonomous vehicle in a real test field are implemented to validate the effectiveness of the proposed approaches. Results show that the AUC_ROC of the proposed ALDSAE is over 20% higher in average than several traditional anomaly detectors, and the mean explanation accuracy of the residual explainer achieves similar performance with the widely employed kernel SHAP with more than 99% reduction in mean response time.

Coordination of vehicles in on-ramp merging scenarios is crucial for preventing traffic bottlenecks, congestion, and accidents. Connected and automated vehicles (CAVs) with vehicle-to-everything (V2X) communication have the potential to improve vehicle coordination and enhance the safety and efficiency of on-ramp merging. While the existing research mainly concentrates on single-lane mainline scenarios, and its applicability to multi-lane on-ramp merging scenario is limited. To address this gap, a multi-lane on-ramp merging strategy is proposed, integrating dynamic lane-changing decision-making and quasi-uniform B-spline trajectory optimization. Initially, a multi-lane dynamic lane-changing decision model is developed, leveraging lane-changing benefits and traffic flow assisted decision-making. This approach comprehensively considers vehicle surroundings, combining global road section information to enhance accuracy of lane-changing decision and overall traffic efficiency. Subsequently, a trajectory optimization model based on quasi-uniform B-splines is designed, formulating the optimization problem as a Quadratic Programming (QP) problem to balance trajectory feasibility and computational load. Simulation results reveal that the proposed lane-changing decision model enhances lane-change accuracy and reduces travel delay in different mainline traffic flow proportions and mainline-ramp traffic demands, outperforming the existing model. Moreover, the proposed trajectory method surpasses other methods in safety and comfort during the merging process, with a noticeable improvement in traffic efficiency compared to SUMO's built-in algorithm.

Inappropriate driving behaviours can result in additional fuel consumption and emissions. Drivers can be informed of the accurate signal phase and timing (SPaT) and distance information of the current intersection and downstream intersections via vehicle-to-everything (V2X) communications. The real-time information has been utilized to assist drivers in taking reasonable manoeuvres and gain lots of benefits on fuel consumption and emissions in some existing studies. In order to cooperatively address the optimization problem on the signalized arterial corridors, this paper presents an eco-driving optimization model considering preceding SPaT and position information. This model can be applied to pass two successive traffic signals cooperatively during green phase. In this study, a multi-stage optimal approach is proposed to minimize the fuel consumption. Field experiments are carried out for comparative analysis between the connected vehicle with speed advisory and the uninformed vehicle without speed advisory. The results indicate that the fuel saving of the connected vehicle guided by the dynamic optimization algorithm shows significant improvement. In addition, the rolling optimization among three signalized intersections is conducted and the results show that a considerable improvement can be obtained compared with the one-by-one optimization.

Simultaneous Localization and Mapping (SLAM) is used in solving the problems of localization, navigation, and map construction for autonomous vehicles moving in unknown environments. Place recognition is an inevitable subject in SLAM, and the current lidar-based methods have been popularized for their rising environmental robustness. Currently, the place recognition method with lidar has attracted much attention due to its high environmental robustness. However, many 3D laser point cloud methods for place recognition descriptors construction with the local or global point-cloud data omitting some intrinsic properties of the point clouds. In this article, we propose a place recognition method based on the unitary invariance of the Frobenius norm for utilizing different attributes of ground points and non-ground points. Specifically, the interpretable filtering framework and a dynamic threshold adjustment strategy are raised according to different environments and sensors with intensity and geometric information. As commissioning, the comparative experiments are conducted with CHDloop datasets and public KITTI datasets of different scales and access types. Compared to the original Scan Context (SC) and Intensity Scan Context (ISC) methods, our proposed method achieves higher efficiency while maintaining a improved recall rate and precision. This novel method has been integrated into the existing LiDAR SLAM and formed a new framework FSC_ALOAM that reduces drifts in point-cloud mapping. The entire process improves the accordance of maps at the identical position in auto-driving.

The cooperative lane change among several connected and automated vehicles (CAVs) provides ideas for enhancing the traffic safety and efficiency issues caused by lane changes. However, most of the existing studies mainly focus on the independent analysis of the lateral and longitudinal movements of the lane change without considering the impact of the lateral motion on the longitudinal motion. These works usually assume that the target tracking-preceding vehicles are determined for both the lane change vehicle and the following cooperative vehicle in the target lane. This work proposes a model predictive control (MPC)-based cooperative lane change (CLC) control strategy by considering the preceding vehicle switching and the correlation between the lateral and longitudinal motions. It builds the lateral movement based on the appropriate function curve and integrates this lateral movement in the construction of the coordinated longitudinal motion control strategy by using a set of linear piecewise functions in the design of constraints and objective function of the optimization model to provide smooth preceding vehicles switch. An advanced optimization solver is used to solve the optimization control problem step by step. The proposed strategy is validated based on numerical comparative experiments with two typical lane-changing scenarios. The results show that the proposed control strategy can smoothly complete the preceding vehicle switching during the lane change and quickly realize the stable tracking of the target lane vehicles after changing lanes.