This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of ...automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. A discrete-time preview controller is then designed on the top of a linear augmented error system, in which the disturbances within a finite preview window are augmented as part of the state vector. The obtained optimal steering control law is in an analytic form and consists of two parts: 1) a feedback control responding to tracking errors and 2) a feedforward control dealing with the future road curvatures. The designed control's nature, capacity, computation load, and underlying mechanism are revealed by the analysis of system responses in the time domain and the frequency domain, theoretical steady-state error, and comparison with the model predictive control (MPC). The algorithm was implemented on an automated vehicle platform, a hybrid Lincoln MKZ. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.
Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the ...recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.
We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regression, occupancy ...maps, and 1-step stochastic policies. We instead frame the trajectory prediction problem as classification over a diverse set of trajectories. The size of this set remains manageable due to the limited number of distinct actions that can be taken over a reasonable prediction horizon. We structure the trajectory set to a) ensure a desired level of coverage of the state space, and b) eliminate physically impossible trajectories. By dynamically generating trajectory sets based on the agent's current state, we can further improve our method's efficiency. We demonstrate our approach on public, real world self-driving datasets, and show that it outperforms state-of-the-art methods.
Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential ...functions to different types of obstacles and road structures and plans the path based on these potential functions. It does not, however, include the vehicle dynamics in the path-planning process. On the other hand, an optimal path-planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints and not with any arbitrary function. A model predictive path-planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path-planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path-planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path-planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path-planning system plans paths corresponding to their importance and priorities.
Today's modern vehicles contain anywhere from sixty to one-hundred sensors and exhibit the characteristics of Cyber-Physical-Systems (CPS). There is a high degree of coupling, cohesiveness, and ...interactions among vehicle's CPS components (e.g., sensors, devices, systems, systems-of-systems) across sensing, communication, and control layers. Cyber-attacks in the sensing or communication layers can compromise the security of the control layer. This paper provides a detailed review of potential cyber threats related to the sensing layer. Notably, the focus is mainly towards two categories of sensors: vehicle dynamics sensors (e.g., Tire Pressure Monitoring Systems (TPMS), magnetic encoders, and inertial sensors) and environment sensors (e.g., Light Detection and Ranging (LiDAR), ultrasonic, camera, Radio Detection and Ranging (Radar) systems, and Global Positioning System (GPS) units). The paper also offers perspectives through existing countermeasures from literature and stresses the need for data-driven cybersecurity solutions.
Antilock braking systems are one of the most important safety systems for wheeled vehicles. They reduce the braking distance and, most importantly, help the user maintain controllability and ...steerability of the vehicle. This brief extends and adapts the concept of ABSs to tracked vehicles, in particular to snowmobiles. Snowmobiles are an interesting development platform for two main reasons: 1) track dynamics, despite being analogous to tire dynamics, present important differences that help understanding the features of the control algorithm and 2) snowmobiles are simple and rugged vehicles with a limited set of sensors, making the design of an effective control system challenging. This brief designs a track-deceleration-based ABS algorithm and tests it both in straight riding and cornering. The analysis shows that on snowmobiles, ABSs have negligible advantages in term of stopping distance, but are beneficial in terms of steerability and stability, especially during cornering.
CubeSLAM: Monocular 3-D Object SLAM Yang, Shichao; Scherer, Sebastian
IEEE transactions on robotics,
2019-Aug., 2019-8-00, Letnik:
35, Številka:
4
Journal Article
Recenzirano
Odprti dostop
In this paper, we present a method for single image three-dimensional (3-D) cuboid object detection and multiview object simultaneous localization and mapping in both static and dynamic environments, ...and demonstrate that the two parts can improve each other. First, for single image object detection, we generate high-quality cuboid proposals from two-dimensional (2-D) bounding boxes and vanishing points sampling. The proposals are further scored and selected based on the alignment with image edges. Second, multiview bundle adjustment with new object measurements is proposed to jointly optimize poses of cameras, objects, and points. Objects can provide long-range geometric and scale constraints to improve camera pose estimation and reduce monocular drift. Instead of treating dynamic regions as outliers, we utilize object representation and motion model constraints to improve the camera pose estimation. The 3-D detection experiments on SUN RGBD and KITTI show better accuracy and robustness over existing approaches. On the public TUM, KITTI odometry and our own collected datasets, our SLAM method achieves the state-of-the-art monocular camera pose estimation and at the same time, improves the 3-D object detection accuracy.
In order to achieve the stability and safety of the autonomous vehicle while reducing the control cost, a driving strategy for uncertain autonomous vehicle is presented. There are many uncertainties ...in the autonomous vehicle driving, and the boundary of the uncertainty maybe unknown. To solve this problem, a gated leakage type adaptive robust control based on the tracking deviation is developed. The salient feature of the control lies in the novel leakage mechanism designs. The leakage mechanism is designed to provide gated value for the leakage to prevent excessive control effort. The control system ensures the performance of the autonomous vehicle in terms of lateral and yaw displacement, which in turn prevents the vehicle from sideslip even in uncertainty. Compared with the constant leakage type adaptive robust control and Linear Quadratic Regulator (LQR) control, the effectiveness and superiority of the proposed control method are verified
This paper provides a new solution for path following control of autonomous ground vehicles. <inline-formula> <tex-math notation="LaTeX">\mathcal {H}_{2} </tex-math></inline-formula> control problem ...is considered to attenuate the effect of the road curvature disturbance. To this end, we formulate a standard model from the road-vehicle dynamics, the a priori knowledge on the road curvature, and the path following specifications. This standard model is then represented in a Takagi-Sugeno fuzzy form to deal with the time-varying nature of the vehicle speed. Based on a static output feedback scheme, the proposed method allows avoiding expensive vehicle sensors while keeping the simplest control structure for real-time implementation. The concept of <inline-formula> <tex-math notation="LaTeX">\mathcal {D}- </tex-math></inline-formula>stability is exploited using Lyapunov stability arguments to improve the transient behaviors of the closed-loop vehicle system. In particular, the physical upper and lower bounds of the vehicle acceleration are explicitly considered in the design procedure via a parameter-dependent Lyapunov function to reduce drastically the design conservatism. The proposed <inline-formula> <tex-math notation="LaTeX">\mathcal {H}_{2} </tex-math></inline-formula> design conditions are expressed in terms of linear matrix inequalities (LMIs) with a single line search parameter. The effectiveness of the new path following control method is clearly demonstrated with both theoretical illustrations and hardware experiments under real-world driving situations.
We propose two novel dynamic event-triggered control laws to solve the average consensus problem for first-order continuous-time multiagent systems over undirected graphs. Compared with the most ...existing triggering laws, the proposed laws involve internal dynamic variables, which play an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Moreover, some existing triggering laws are special cases of ours. For the proposed self-triggered algorithm, continuous agent listening is avoided as each agent predicts its next triggering time and broadcasts it to its neighbors at the current triggering time. Thus, each agent only needs to sense and broadcast at its triggering times, and to listen to and receive incoming information from its neighbors at their triggering times. It is proved that the proposed triggering laws make the state of each agent converge exponentially to the average of the agents' initial states if and only if the underlying graph is connected. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.
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