An autonomous vehicle sideslip angle estimation algorithm is proposed based on consensus and vehicle kinematics/ dynamics synthesis. Based on the velocity error measurements between the reduced Inertial Navigation System (R-INS) and the global navigation satellite system (GNSS), a velocity-based Kalman filter is formalized to estimate the velocity errors, attitude errors, and gyro bias errors of the R-INS. The observability issue of the heading error, which affects sideslip estimation, is analyzed. Then, to enhance the observability and improve the estimation accuracy of the heading error under normal driving conditions, a consensus Kalman information filter is developed to synthesize the vehicle kinematics and dynamics and estimate the heading error. Within the developed consensus framework, one node augments a novel heading error measurement from a linear vehicle-dynamic-based sideslip estimator and another node adopts the heading error from the GNSS course. Next, based on the vehicle lateral excitation level, a weighting scheme is proposed to fuse the error state estimates from the velocity-based and consensus Kalman state observers. The stability of the proposed state observers is also investigated. Comprehensive experimental studies, including critical slalom, slight/normal double lane change, and normal driving maneuvers, were conducted to verify the proposed estimation framework; they confirm the reliability and accuracy of the estimator in various automated driving conditions even in comparison with state-of-the-art methods that utilize more measurements (dual-antenna GNSS). Also, this novel multisensor framework is extendable to leverage speed information from other sensors such as cameras and light detection and ranging (LiDAR) to increase reliability and accuracy.

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.

3D-LiDAR-based cooperative perception has been generating significant interest for its ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues that can be problematic for single-vehicle perception. Despite its effectiveness in overcoming various challenges, cooperative per-ception's performance can still be affected by the aforementioned issues when Connected Automated Vehicles (CAVs) operate at the edges of their sensing range. Our proposed approach called HYDRO-3D aims to improve object detection performance by explicitly incorporating historical object tracking information. Specifically, HYDRO-3D combines object detection features from a state-of-the-art object detection algorithm (V2X-ViT) with historical information from the object tracking algorithm to infer objects. Afterward, a novel spatial-temporal 3D neural network performing global and local manipulations of object-tracking historical data is applied to generate the feature map to enhance object detection. The proposed HYDRO-3D method is comprehensively evaluated on the state-of-the-art V2XSet. The qualitative and quantitative experiment results demonstrate that the HYDRO-3D can effectively utilize the object tracking information and achieve robust object detection performance. It outperforms the SOTA V2X-ViT by 3.7% in AP@0.7 of object detection for CAVs and can also be generalized to single-vehicle object detection with 4.5% improvement in AP@0.7.

The sideslip angle and attitude are crucial for automated driving especially for chassis integrated control and environmental perception. In this article an inertial measurement unit (IMU)-based automated vehicle body sideslip angle and attitude estimation method aided by low-sample-rate global navigation satellite system (GNSS) velocity and position measurements using parallel adaptive Kalman filters is proposed. This method can estimate the sideslip angle and attitude simultaneously and is robust against the vehicle parameters and road friction even as the vehicle enters critical maneuvers. First, based on the acceleration and angular rate from the six-dimensional inertial measurement unit, the attitude, velocity and position (AVP) are integrated with the navigation coordinates and the AVP error dynamics and observation equations of the integration results are developed. Second, parallel innovation adaptive estimation (IAE)-based Kalman filters is designed to estimate the AVP error of the integration method to address the issues of the GNSS low sampled rate and abnormal measurements. Then the AVP error is forwarded to the AVP integration to compensate the accumulated error. To improve the heading angle estimation accuracy, the heading error is estimated by a decoupled IAE-based Kalman filter aided by GNSS heading. In addition, time synchronization of the IMU and GNSS is realized through hardware based on the pulse per second signal of the GNSS receiver and the spatial synchronization is achieved by a direct compensation method. Lastly, the sideslip angle and attitude estimation method is validated by a comprehensive experimental test including critical double lane change and slalom maneuvers. The results show that the estimation error of the longitudinal velocity and lateral velocity is smaller than 0.1 m/s (1σ), and the estimation error of the sideslip angle is smaller than 0.15° (1σ).