Testing cloud-controlled intelligent connected vehicles (ICVs) requires simulation environments that faithfully emulate both vehicle behavior and realistic communication latencies. This paper proposes a latency-aware co-simulation platform integrating CarMaker and Vissim to evaluate safety and comfort under real-world vehicle-to-cloud (V2C) latency conditions. Three communication latency models, derived from empirical 5G measurements in China and Hungary, are incorporated and statistically modeled using Gamma distributions. A proactive conflict module (PCM) is proposed to dynamically control background vehicles and generate safety-critical scenarios. The platform is validated through experiments involving an exemplary system under test (SUT) across eight testing conditions combining two PCM modes (enabled/disabled) and four latency conditions (none, China, Hungary, abnormal). Safety and comfort are assessed using metrics including collision rate, distance headway, post-encroachment time, and the spectral characteristics of longitudinal acceleration. Results show that the PCM effectively increases driving environment criticality, while V2C latency reduces ride comfort and, under extreme driving conditions, further aggravates safety-critical scenarios. These findings confirm the platform’s effectiveness in systematically evaluating cloud-controlled ICVs under diverse testing conditions.

Deep Reinforcement Learning (DRL) is becoming a prominent method for autonomous driving due to its strong capability to generate complex driving policy. However, DRL motion planning still has limitations in safety performance including learning quality, convergence speed and the safety guarantee. To this end, this work proposes a safety-enhanced deep reinforcement learning method with dynamic safety guidance (DSG-DRL) for lane-change motion planning. It bears the following key features: 1) Able to learn a safer DRL driving policy by additionally including potentially unsafe behaviors; 2) Able to accelerate learning a safe policy by making dangerous driving experiences impressive; 3) Able to further enhance the driving safety by avoiding unexpected reckless action. The proposed DSG-DRL motion planner dares to make mistakes to learn the safe driving policy better and faster. By evaluating anticipated risk, it learns not only from the maneuvers right at the moments of collisions, but also from the dangerous maneuvers leading towards collisions. Besides, risk driving experiences are enhanced with additional memory batches and sampling prioritization. Moreover, reckless actions can be prevented by dynamic constraints both in training and testing, which further improves the safety performance. Simulation validation shows that the proposed method can learn a safer driving policy with faster convergence speed, achieving the high safety performance while keeping the driving efficiency.

We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or bird’s eye view (BEV) images, we represent both LiDAR and 4D radar point clouds using voxel features, which more effectively capture 3D shape information. Subsequently, we propose the Latent Voxel Diffusion Model (LVDM), which performs the diffusion process in the latent space. Additionally, a novel Latent Point Cloud Reconstruction (LPCR) module is utilized to reconstruct point clouds from high-dimensional latent voxel features. As a result, R2LDM effectively generates LiDAR-like point clouds from paired raw radar data. We evaluate our approach on two different datasets, and the experimental results demonstrate that our model achieves 6- to 10-fold densification of radar point clouds, outperforming state-of-the-art baselines in 4D radar point cloud super-resolution. Furthermore, the enhanced radar point clouds generated by our method significantly improve downstream tasks, achieving up to 31.7% improvement in point cloud registration recall rate and 24.9% improvement in object detection accuracy.

The path tracking performance of global vehicle targets (GVTs) is crucial in closed-field testing for autonomous vehicles (AVs). However, the uncertain latency of 5G communication, when GVTs receive control commands from the cloud server, is likely to impact their path tracking performance adversely. To address this issue, we propose a cloud-based predictive path tracking control strategy considering the uncertain latency of 5G communication. Firstly, the 5G communication dataset from the closed test field is analyzed, and an LSTM-attention latency prediction model is established and validated. Subsequently, a nonlinear model predictive control (NMPC) algorithm that incorporates communication latency is formulated. Finally, real-world tests are conducted, and the results demonstrate that the proposed method significantly improves latency prediction accuracy and path tracking performance of GVTs.

Track testing plays a critical role in the safety evaluation of autonomous driving systems (ADS), as it provides a real-world interaction environment. However, the inflexibility in motion control of object targets and the absence of intelligent interactive testing methods often result in pre-fixed and limited testing scenarios. To address these limitations, we propose a novel 5G cloud-controlled track testing framework, Real-world Troublemaker. This framework overcomes the rigidity of traditional pre-programmed control by leveraging 5G cloud-controlled object targets integrated with the Internet of Things (IoT) and vehicle teleoperation technologies. Unlike conventional testing methods that rely on pre-set conditions, we propose a dynamic game strategy based on a quadratic risk interaction utility function, facilitating intelligent interactions with the vehicle under test (VUT) and creating a more realistic and dynamic interaction environment. The proposed framework has been successfully implemented at the Tongji University Intelligent Connected Vehicle Evaluation Base. Field test results demonstrate that Troublemaker can perform dynamic interactive testing of ADS accurately and effectively. Compared to traditional methods, Troublemaker improves scenario reproduction accuracy by 65.2%, increases the diversity of interaction strategies by approximately 9.2 times, and enhances exposure frequency of safety-critical scenarios by 3.0 times in unprotected left-turn scenarios.

Locating 3D objects from a single RGB image via Perspective-n-Point (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, allowing for partial learning of 2D-3D point correspondences by backpropagating the gradients of pose loss. Yet, learning the entire correspondences from scratch is highly challenging, particularly for ambiguous pose solutions, where the globally optimal pose is theoretically non-differentiable w.r.t. the points. In this paper, we propose the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose with differentiable probability density on the SE(3) manifold. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle generalizes previous approaches, and resembles the attention mechanism. EPro-PnP can enhance existing correspondence networks, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation benchmark. Furthermore, EPro-PnP helps to explore new possibilities of network design, as we demonstrate a novel deformable correspondence network with the state-of-the-art pose accuracy on the nuScenes 3D object detection benchmark.

Driving through traffic scenes with occlusions safely, efficiently and comfortably remains a huge challenge for autonomous vehicles (AVs). The omnipresence of blind spots, where emergencies may arise unexpectedly, requires AVs to drive with adequate precautions. However, existing planning algorithms often resort to excessive conservatism that leads to navigation-disrupting deadlocks. To address these challenges, we adopt the concept of phantom obstacles, imparting them with uncertainties. At that point, we propose an integrated lateral and longitudinal trajectory planning method to emulate human-like precautionary driving styles, and adopt stochastic model predictive control (SMPC) to model various uncertainties of obstacles. Furthermore, we analyze deadlock causation mechanisms and present a novel quantified risk assessment method featuring an innovative adaptation of ST projection for occlusion-aware planning. This approach formulates a driving strategy that significantly reduces the likelihood of deadlock, enhancing the AV’s ability to navigate effectively in occluded scenarios. Case analysis and statistical research across diverse scenarios, featuring different objects in various typical settings, validate the safety, efficiency and comfort of the proposed planning results. These findings underscore the potential of our approach to advance the capabilities of autonomous vehicles in navigating complex and occluded driving environments.

Lane-change is a common maneuver. However, completely egocentric lane-changing behavior may increase potential driving risks and cause oscillations in the movement of surrounding vehicles, referred to as disharmony. To improve the overall safety and efficiency of both ego vehicle and their surroundings, this paper proposes a convergent harmonious reinforcement learning (CHRL) approach to generate harmonious lane-changing strategies. It introduces a game-based model to measure the overall harmony cost. On this basis, a prosocial critic network is established to guide the policy toward harmony by decreasing the harmony cost. Meanwhile, CHRL identifies and penalizes discordant behaviors that may lead to high risk, accelerating the RL agent’s learning of harmonious driving strategies through expert demonstrations of the game model. Simulation and real-data tests validate that CHRL, compared to other lane-change methods and human drivers, improves the overall harmony of lane changes for autonomous vehicles.

Localization is crucial for Intelligent Transportation Systems (ITS) as it provides the precise position and orientation necessary for the operation of autonomous vehicles. Monocular vision-inertial odometry (VIO) has gained extensive application due to its high accuracy and cost-effectiveness. In this paper, we propose 4DRE-VIO, a novel 4D radar-enhanced monocular VIO system that achieves accurate and cost-effective pose estimation for autonomous vehicles in urban environments. By tightly integrating 4D radar measurements, we address the issues of scale ambiguity, scale drift, and dynamic object interference associated with monocular VIO. Specifically, we integrate 4D radar Doppler velocity with non-holonomic constraints (NHC) to provide a reliable velocity observation. Moreover, this velocity is directly used to construct a monocular scale observation, ensuring stable and accurate scale estimation. To mitigate the impact of dynamic objects on VIO, which is based on static environment assumptions, we introduce a 4D radar-enhanced visual semantic segmentation algorithm for dynamic object recognition. Additionally, we utilize a factor graph to tightly integrate measurements from the 4D radar, IMU, and monocular camera. The design of adjacent factors and optimal co-visible factors ensures the efficient use of visual information. An adaptive fusion strategy based on driving conditions maximizes the benefits of each sensor. We validate the effectiveness of the proposed method through extensive testing on a large dataset collected in urban environments, including open streets and residential districts. Compared to typical VIO and 4D radar-related odometry, our method demonstrates superior performance on our dataset and the public dataset.

This paper presents the DiffusionRegPose, a novel approach to multi-person pose estimation that converts a one-stage, end-to-end keypoint regression model into a diffusion-based sampling process. Existing one-stage deterministic re-gression methods, though efficient, are often prone to missed or false detections in crowded or occluded scenes, due to their inability to reason pose ambiguity. To address these challenges, we handle ambiguous poses in a generative fashion, i.e., sampling from the image-conditioned pose distributions characterized by a diffusion probabilistic model. Specifically, with initial pose tokens extracted from the image, noisy pose candidates are progressively refined by inter-acting with the initial tokens via attention layers. Extensive evaluations on the COCO and CrowdPose datasets show that DiffusionRegPose clearly improves the pose accuracy in crowded scenarios, as evidenced by a notable 4. 0 AP in-crease in the APHmetric on the CrowdPose dataset. This demonstrates the model's potential for robust and precise human pose estimation in real-world applications. Code will be available at https://github.com/cici203IDiffusionRegPose.

In this paper, we propose a cloud control method with communication delay prediction for intelligent connected vehicles (ICVs), which not only constructs a prediction model using real-world 5G communication delay data, but also evaluate the effectiveness of the cloud control method considering delay in typical application scenarios. Firstly, for the application data interaction of 5G vehicle-to-network-to-vehicle (V2N2V) full-link, we collect a large amount of communication delay data through vehicle test. Then, a novel data-driven delay prediction method based on the Long Short-Term Memory (LSTM) network is introduced. Finally, a cloud control method considering communication delay is constructed at unsignalized intersection. The test results show that our method can not only achieve high delay prediction accuracy, but also significantly reduce vehicle velocity fluctuations and avoid collisions.

Autonomous driving requires efficient and safe decision making and motion planning in dynamic and uncertain environments. Future movement of surrounding vehicles is often difficult to represent. Besides, most existing studies consider decision making and planning/control separately. Both them may lead to the oscillation and unsafe for autonomous driving. This paper proposes an integrated framework of decision making and motion planning with oscillation-free capability. The proposed approach overcomes the shortcomings of autonomous driving for lane change/keeping maneuvers and is able to: i) make oscillation-free behavior decisions given biased prediction; ii) cut through in the traffic efficiently and safely when being in squeezed; iii) accelerate computation efficiency by building a state transfer model based on prediction uncertainty; iv) reduce the dissonance between decision-making and motion planning. A belief decision planner is designed with the uncertainty of the prediction trajectories. Lateral and longitudinal drivable corridors including the reference state and the related boundary constraints are built, which provide better suited information for planning to solve the optimal motion sequence more quickly and stably, and improve its consistency with decision module. Finally, the problem is formulated as an optimal control problem considering the vehicle dynamics and some soft constraints and the motion trajectory is solved by OSQP. Simulation and experimental tests are implemented to evaluate the feasibility and effectiveness of the proposed approach. Test results show that the integrated approach can make proper, safe and continuous decision and planning for autonomous vehicles and the calculation time is very low.

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.

Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, so that 2D-3D point correspondences can be partly learned by backpropagating the gradient w.r.t. object pose. Yet, learning the entire set of unrestricted 2D-3D points from scratch fails to converge with existing approaches, since the deterministic pose is inherently non-differentiable. In this paper, we propose the EPro-PnP a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose on the SE(3) manifold, essentially bringing categorical Softmax to the continuous domain. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle unifies the existing approaches and resembles the attention mechanism. EPro-PnP significantly outperforms competitive baselines, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation and nuScenes 3D object detection benchmarks.3

The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale of the reconstructed map, we formulate an optimization problem to make the scaled dimensions of objects conform to the distribution of their sizes in the physical world, without relying on any prior information about gravity direction. The dual quadric is adopted to represent objects for its ability to describe objects compactly and accurately, thus providing reliable dimensions for scale estimation. In the proposed monocular object-level SLAM system, semantic objects are initialized first from fitted 3-D oriented bounding boxes and then further optimized under constraints of 2-D detections and 3-D map points. Experiments on indoor and outdoor public datasets show that our approach outperforms existing methods in terms of accuracy and robustness.

The tire-road peak adhesion coefficient (TRPAC) describes the tire adhesion limit that a road can provide. The TRPAC is a key parameter for precise vehicle motion control and an important basis for decision-making and planning of intelligent vehicles. Considering the critical and difficult problems in the estimation of TRPAC, such as slow convergence and low accuracy, a TRPAC estimation method based on the fusion of vehicle dynamics and machine vision is proposed in this paper. Based on the observability theory of nonlinear systems, local weak observability of the dynamics-based estimator is analyzed to explain the limitation of a single dynamics-based estimator. The framework of dynamics-image-based fusion estimator is then proposed, including the fusion of data, model and decision levels. A dynamics-based fusion estimator is designed by considering the coupling relationship of longitudinal and lateral tire forces to adapt the conditions of complex excitations. Start-and-stop strategy for the dynamics-based fusion estimator is designed by setting excitation thresholds for different types of road surfaces, which are identified using vision information. Parameter self-tuning for the dynamics-based fusion estimator based on the image-based estimator is proposed to improve convergence speed and reduce oscillation. The results of the simulation and vehicle test show that the road estimation error of the proposed method is within 0.03 and the convergence time is within 0.5 s. Compared with other existing estimators, the fusion estimator achieved better accuracy, sensitivity and stability, particularly when complex excitations were present.

This paper proposes G-VIDO, a vehicle dynamics, and intermittent Global Navigation Satellite System (GNSS)-aided visual-inertial state estimator, to address the state estimation problem of autonomous vehicle localization (i.e., position and orientation estimation in the global coordinate system) under various GNSS states. A dynamics pre-integration theory is proposed on the basis of a two-degree-of-freedom (DOF) vehicle dynamics model, and dynamics constraints are built in the optimization back-end, considering the unobservable problem of the monocular visual-inertial system under degenerate motions. The proposed highly nonlinear system can be robustly initialized by loosely aligning the monocular structure from motion (SfM) results, pre-integrated IMU measurements, and vehicle motion information. GNSS is used for reference frame transformation and constraint construction in the sliding window. The cumulative error can be corrected with the aid of GNSS, and the vehicle’s position in the global coordinate system can be determined. A GNSS anomaly detection algorithm is proposed to improve the system robustness under intermittent GNSS. Experiments have shown that G-VIDO can provide real-time, robust, and seamless localization in multiple GNSS states, with an RMSE of less than 30 cm (with GNSS). Moreover, we proved that the initialization and local odometry modules in G-VIDO outperform several state-of-the-art VIO systems and our preliminary work VINS-Vehicle.

Object localization in 3D space is a challenging aspect in monocular 3D object detection. Recent advances in 6DoF pose estimation have shown that predicting dense 2D-3D correspondence maps between image and object 3D model and then estimating object pose via Perspective-n-Point (PnP) algorithm can achieve remarkable localization accuracy. Yet these methods rely on training with ground truth of object geometry, which is difficult to acquire in real outdoor scenes. To address this issue, we propose MonoRUn, a novel detection framework that learns dense correspondences and geometry in a self-supervised manner, with simple 3D bounding box annotations. To regress the pixel-related 3D object coordinates, we employ a regional reconstruction network with uncertainty awareness. For self-supervised training, the predicted 3D coordinates are projected back to the image plane. A Robust KL loss is proposed to minimize the uncertainty-weighted reprojection error. During testing phase, we exploit the network uncertainty by propagating it through all downstream modules. More specifically, the uncertainty-driven PnP algorithm is leveraged to estimate object pose and its covariance. Extensive experiments demonstrate that our proposed approach outperforms current state-of-the-art methods on KITTI benchmark.1

Hazard analysis is a quite significant step to ensure vehicle safety in the early stage of vehicle development according to current standards. However, the complexity of the Advanced Driving Assistance System (ADAS) and Automated Driving Systems (ADS), which consist of various software and hardware components, makes it difficult to identify system hazards. Nowadays, System-Theoretic Process Analysis (STPA), a hazard analysis method for complex systems, is applied to ADAS, and simple ADS gradually and proved applicable. This paper introduced Finite State Machine (FSM) to complement the STPA for its weakness in analyzing high-level autonomous vehicles with multiple automated modes and functions. Firstly, previous applications of STPA to ADAS and ADS and their limitations are analyzed. Secondly, the hazardous event is defined. An extended method combining STPA and FSM is proposed to model the vehicle states and environmental conditions and analyze unexpected behaviors. Finally, a case study on an autonomous vehicle is given to compare the traditional STPA and the extended method. Comparing with the traditional STPA, the proposed method can identify more hazardous events and give more detailed information about hazardous events to generate testing scenarios.

Motion planning and vehicle prediction play an important role for autonomous vehicle, which aims to guarantee the driving safety under cut-in scenarios. This paper presents a hybrid prediction model for computing the future trajectory of the surrounding vehicles and a dynamic speed planner based on model predictive control to avoid collisions. Firstly, the hybrid prediction model combines the physics-based model and behavior-based model through Mamdani fuzzy logic. The predicted physic trajectory is computed using the constant yaw rate and velocity vehicle model. In addition, the prediction of driving intention is accomplished by using information of the difference between current motion and driving lanes. Furthermore, the predicted behavior trajectory is selected from the candidate quintic polynomial trajectory cluster through the designed cost function. Then, Gaussian propagation is applied at the fusion trajectory to compute the uncertainty distribution. Secondly, the dynamic speed planner based on model predictive control provides the optimal control command for the collision avoidance maneuver, which considers the future trajectory distribution of surrounding vehicles. Finally, the effectiveness of the proposed method is verified through simulation in different cut-in scenarios.

Taking advantage of available measurement in Internet of Things (IoT) for intelligent transportation systems, a sideslip angle estimation method for autonomous vehicles is presented and experimentally verified by fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU), and by constructing an observability index (OI). The correlation between the vehicle sideslip error and the inertial navigation system (INS) heading error is presented first. Then, the observability for the heading error in a velocity-based Kalman filter is discussed and a novel index is defined to check the observability of the heading error. The course from a single antenna GNSS in an autonomous vehicle is augmented to estimate the heading error when the observability of the heading error is low. To reject the course measurement for scenarios that include sideslip movement, a binary hypothesis test approach is applied to indicate whether the vehicle is sidesliping. In addition, based on the OI and the sideslip indicator, a hybrid feedback strategy is designed for the heading error correction. To improve the convergence rate of the heading error in the velocity-based Kalman filter, a tuning strategy is presented. The stochastical observability of the designed Kalman observer is investigated for known and stochastic initial conditions. Finally, the proposed sideslip angle estimator is experimentally validated through a vehicle test platform in critical driving scenarios. The results confirm that the proposed OI can effectively identify when the heading error is observable, and also corroborate the effectiveness of the hybrid feedback strategy and adaptation method in the Kalman observer.

Detecting and locating surrounding vehicles robustly and efficiently are essential capabilities for autonomous vehicles. Existing solutions often rely on vision-based methods or 3D LiDAR-based methods. These methods are either too expensive in both sensor pricing (3D LiDAR) and computation (camera and 3D LiDAR) or less robust in resisting harsh environment changes (camera). In this work, we revisit the LiDAR based approaches for vehicle detection with a less expensive 2D LiDAR by utilizing modern deep learning approaches. We aim at filling in the gap as few previous works conclude an efficient and robust vehicle detection solution in a deep learning way in 2D. To this end, we propose a learning based method with the input of pseudo-images, named Cascade Pyramid Region Proposal Convolution Neural Network (Cascade Pyramid RCNN), and a hybrid learning method with the input of sparse points, named Hybrid Resnet Lite. Experiments are conducted with our newly 2D LiDAR vehicle dataset recorded in complex traffic environments. Results demonstrate that the Cascade Pyramid RCNN outperforms state-of-the-art methods in accuracy while the proposed Hybrid Resnet Lite provides superior performance of the speed and lightweight model by hybridizing learning based and non-learning based modules. As few previous works conclude an efficient and robust vehicle detection solution with 2D LiDAR, our research fills in this gap and illustrates that even with limited sensing source from a 2D LiDAR, detecting obstacles like vehicles efficiently and robustly is still achievable.

The work analyzes dynamic responses of a healthy plant under optimal switching data-injection attacks on sensors and develops countermeasures from the vantage point of optimal control. This is approached in a cyber-physical system setting, where the attacker can inject false data into a selected subset of sensors to maximize the quadratic cost of states and the energy consumption of the controller at a minimal effort. A 0-1 integer program is formulated, through which the adversary finds an optimal sequence of sets of sensors to attack at optimal switching instants. Specifically, the number of compromised sensors per instant is kept fixed, yet their locations can be dynamic. Leveraging the embedded transformation and mathematical programming, an analytical solution is obtained, which includes an algebraic switching condition determining the optimal sequence of attack locations (compromised sensor sets), along with an optimal state-feedback-based data-injection law. To thwart the adversary, however, a resilient control approach is put forward for stabilizing the compromised system under arbitrary switching attacks constructed based on a set of state-feedback laws, each of which corresponds to a compromised sensor set. Finally, an application using power generators in a cyber-enabled smart grid is provided to corroborate the effectiveness of the resilient control scheme and the practical merits of the theory.

This paper proposes a road friction coefficient estimation method based on the fusion of machine vision and vehicle dynamics. A vehicle-mounted camera is used to obtain the front image. Based on the deep learning method, the road surface in the image is segmented and identified to obtain the road type. Besides, a road friction estimator with the tire longitudinal force estimation is designed. With the visual estimation results, a fusion estimation method is designed based on the structural parameter optimization. The results of simulation experiments show that the fusion estimator has the characteristics of fast convergence and high accuracy.

In this paper, we propose VINS-PL-Vehicle, a points and lines-based monocular visual-inertial navigation system(VINS) combined with vehicle kinematics for indoor garage. The indoor garage contains some texture-less regions. Thus, it is difficult to ensure the robustness of VINS only using point features. Therefore, in addition to point features, we also add line features on the pillars, parking slots, and top structures in the garage. Besides, we utilize the accurately known velocity and steering wheel angle from the vehicle chassis to form the kinematic constraints between image frames, which can not only solve the problem that the scale is not observable due to insufficient excitation of accelerometer when the vehicle is running at a constant speed, but also utilize its drift-free characteristics to improve the accuracy of system initialization and optimization. The vehicle tests show that our algorithm can achieve significantly higher positioning accuracy than VINS-Mono in the indoor garage.

As basic modules of intelligent vehicle, path planning and its tracking have been developed rapidly. However, the trajectory generated by the traditional path-speed decoupled planning method is not feasible in the time domain. In this paper, a method based on improved RRT is proposed to achieve efficient planning and smoothing. In order to realize the matching of track points in space and time domain, closed-loop prediction is adopted to know the actually tracked path more accurately. Taking the nonlinear characteristics of lateral and longitudinal dynamics of the vehicle and the saturation of actuators into account, a unified conditional integral control law is designed to guarantee the global asymptotic stability of the tracking error and to avoid the degradation of actuators performance due to the divergence of the integral operation. Simulations and experiments prove that the proposed planning method is more efficient, the control algorithm can effectively track the planned trajectory, and the prediction method can accurately predict the actual driving trajectory, which is very important for collision detection.

A novel trajectory planning method is proposed in this paper for lane change of autonomous vehicle. Since it is difficult to accurately capture the trajectory of other vehicles, which means the trajectory for autonomous vehicle couldn't always easy to generate quickly. Moreover, the motion planning, as a kind of high-dimensional optimization problem with multiple nonlinear constraints, requires lots of resources to find a right solution. Therefore, we present a trajectory monitoring strategy to keep robust in lane change scenario, which generates the lane change and monitoring trajectory at the same time. If the former does not produce a safe trajectory or is time out, the monitoring trajectory will be taken as the result output. To meet the constraints of vehicle's motion and real-time requirements, B-spline-based method will be employed to plan a continuous curvature path. And RRT-based method works as a supplement for keeping algorithm completeness. Then the monitory trajectory mainly obeys collision-free requirements, which computes deceleration that keeps vehicle stability. The results illustrate that both B-spline-based method and RRT-based could generate curvature continuous and meet the limitation for motion, however, both have the possibility of timeout. Especially, there are challenge to the success rate as environment becomes more complex.

Anti-slip regulation (ASR) is one of the research focus in the field of active safety of electric vehicle. An ASR algorithm adaptive to road condition is proposed in this paper based on 4WD electric vehicle with in-wheel motors. The controller based on anti-windup sliding mode control is robust to wheel parameter uncertainty. The longitudinal velocity estimator based on the fusion of dynamics method and kinematics method is adopted to reduce the velocity estimation error. The road slope is estimated using recursive least square with forgetting factor and the longitudinal acceleration sensor information is calibrated by the road slope estimation for slope adaptive velocity estimation. At the same time, a road coefficient estimator is adopted to estimate road condition using improved Burckhardt model, so the optimal reference slip ratio is selected according to the estimated road adhesion coefficient for the maximum driving efficiency and the realization of adaptive anti-slip regulation. Multi-condition simulations show that the controller is adaptive to road changes, and it can suppress wheel slip ratio and ensure the vehicle stability.

Considering vehicle sideslip angle estimation difficulty under severe driving conditions with dynamic model based method due to vehicle nonlinear characteristic and parameter uncertainty, a novel kinematic model based method is proposed with the fusion of intelligent vehicle sensors. The state space models of the vehicle yaw angle and the roll angle are constructed based on the IMU and the lateral arrangement of the dual-GPS. In order to reduce the weight of the previously estimated value, an adaptive fading Kalman filtering algorithm is adopted to improve the filtering effect on the yaw and roll angle. A nonlinear adaptive observer is constructed to estimate the vehicle sideslip angle with the integration of the road line information from the camera, velocity from the GPS and acceleration/ angular velocity from the IMU. Furthermore, compared with the IMU, the information obtained from the GPS and the camera can't be utilized directly as large measurement delay. Thus, an observer-predictor is developed with multi-sensor fusion to handle the measurement delay problem. Finally, the proposed algorithm is validated through co-simulation under different maneuvers.

Skid steering vehicle has larger wheel slip ratio when steering than straight driving. Due to the lack of accurate tire model in practical application, the slip coefficient is difficult to be quantitatively studied. However, there would be a large steady state error on the skid steering vehicle, which using the wheel speed difference control if the coefficient was ignored. Aiming at this problem, a controller overcoming integral saturation for steering control based on proportional integral control method is designed, which can correct the wheel slip coefficient in real time without the tire model. In the end, the effectiveness of the algorithm is verified by real vehicle test.

In this paper, an estimation method for attitude and lateral velocity for automated vehicle has been proposed using a six degree of freedom inertial measurement unit(IMU) aided by vehicle dynamics. This estimation method makes full use of the advantage of the IMU and vehicle dynamics and could run autonomously without aid from other outside information such as GNSS or camera. Based on Kalman filter theory, three observers have been developed: a kinematic model based attitude observer for pitch angle and roll angle using IMU, a kinematic model based lateral velocity observer for lateral velocity using IMU and a dynamic model based observer using vehicle dynamics. In small excitation condition, the estimated lateral velocity from dynamic model based observer is more reliable and it is forwarded to the two kinematic model based observers to prevent the accumulated estimation error. In larger excitation condition, the two kinematic model based observers run in open-loop mode. Slalom maneuver and double lane change(DLC) maneuvers have been conducted to validate the estimation method. The experiment results have proved the effectiveness of this estimation method.