Zhenping Sun

Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications

This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.

Self-Learning Cruise Control Using Kernel-Based Least Squares Policy Iteration

This paper presents a novel learning-based cruise controller for autonomous land vehicles (ALVs) with unknown dynamics and external disturbances. The learning controller consists of a time-varying proportional-integral (PI) module and an actor-critic learning control module with kernel machines. The learning objective for the cruise control is to make the vehicles longitudinal velocity follow a smoothed spline-based speed profile with the smallest possible errors. The parameters in the PI module are adaptively tuned based on the vehicles state and the action policy of the learning control module. Based on the state transition data of the vehicle controlled by various initial policies, the action policy of the learning control module is optimized by kernel-based least squares policy iteration (KLSPI) in an offline way. The effectiveness of the proposed controller was tested on an ALV platform during long-distance driving in urban traffic and autonomous driving on off-road terrain. The experimental results of the cruise control show that the learning control method can realize data-driven controller design and optimization based on KLSPI and that the controllers performance is adaptive to different road conditions.

A sampling-based local trajectory planner for autonomous driving along a reference path

In this paper, a state space sampling-based local trajectory generation framework for autonomous vehicles driving along a reference path is proposed. The presented framework employs a two-step motion planning architecture. In the first step, a Support Vector Machine based approach is developed to refine the reference path through maximizing the lateral distance to boundaries of the constructed corridor while ensuring curvature-continuity. In the second step, a set of terminal states are sampled aligned with the refined reference path. Then, to satisfy system constraints, a model predictive path generation method is utilized to generate multiple path candidates, which connect the current vehicle state with the sampling terminal states. Simultaneously the velocity profiles are assigned to guarantee safe and comfort driving motions. Finally, an optimal trajectory is selected based on a specified objective function via a discrete optimization scheme. The simulation results demonstrate the planners capability to generate dynamically-feasible trajectories in real time and enable the vehicle to drive safely and smoothly along a rough reference path while avoiding static obstacles.

Ribbon Model based path tracking method for autonomous land vehicle

To address the path tracking problem of autonomous land vehicle, a new vehicle-road model named “Ribbon Model” is constructed under the constraints of road width and vehicle geometry structure. A new vehicle-road evaluation algorithm is developed based on this model, and new path tracking controller is designed. The difficulties of preview distance selection and parameters tuning with speed of pure following controller are avoided in this controller. Performance of the novel method is verified by simulation and vehicle experiments.

Adaptive speed tracking control for autonomous land vehicles in all-terrain navigation: An experimental study

This paper develops a nonparametric controller with an internal model control (IMC) structure for the longitudinal speed tracking control of autonomous land vehicles by designing a proportional and internal model control (IMC) cascade (P-IMC) controller. An IMC architecture is employed in the inner control loop by establishing a nonparametric longitudinal dynamical model, whereas a P controller is designed for the outer control loop. An approach for estimating the terrain effects and compensating for the model errors is also introduced. The differences from other nonparametric controllers are discussed, and the stability of the P-IMC controller is analyzed and validated experimentally. The P-IMC controller is compared with the SpAM+PI to illustrate its advantages. The experimental results of autonomous all-terrain driving show the effectiveness of the P-IMC controller.

A practical trajectory planning framework for autonomous ground vehicles driving in urban environments

This paper presents a practical trajectory planning framework towards fully autonomous driving in urban environments. Firstly, based on the behavioral decision commands, a reference path is extracted from the digital map using the LIDAR-based localization information. The reference path is refined and interpolated via a nonlinear optimization algorithm and a parametric algorithm, respectively. Secondly, the trajectory planning task is decomposed into spatial path planning and velocity profile planning. A closed-form algorithm is employed to generate a rich set of kinematically-feasible spatial path candidates within the curvilinear coordinate framework. At the same time, the velocity planning algorithm is performed with considering safety and smoothness constraints. The trajectory candidates are evaluated by a carefully developed objective function. Subsequently, the best collision-free and dynamically-feasible trajectory is selected and executed by the trajectory tracking controller. We implemented the proposed trajectory planning strategy on our test autonomous vehicle in the realistic urban traffic scenarios. Experimental results demonstrated its capability and efficiency to handle a variety of driving situations, such as lane keeping, lane changing, vehicle following, and static and dynamic obstacles avoiding, while respecting traffic regulations.

Combining local trajectory planning and tracking control for autonomous ground vehicles navigating along a reference path

In this paper, we develop an integrated local trajectory planning and control scheme for the navigation of autonomous ground vehicles (AGVs) along a reference path with avoidance of static obstacles. Instead of applying traditional cross track-based feedback controllers to steer the vehicle to track the reference path as closely as possible, we decompose the path following task into two subtasks. Firstly, in order to follow the reference path with smooth motions and avoid obstacles as well, we apply an efficient model-based predictive trajectory planner, which considers geometric information of the desired path, kinematic constraints and partial-dynamic constraints to obtain a collision-free, and dynamically-feasible trajectory in each planning cycle. Then, the generated trajectory is fed to the low-level trajectory tracking controller. Relying on the steady-state steering characteristics of vehicles, we develop an internal model controller to track the desired trajectory, while rejecting the negative effects resulting from model uncertainties and external disturbances. Simulation results demonstrate capabilities of the proposed algorithm to smoothly follow a reference path while avoiding static obstacles at a high speed.

Image and Video Region Saliency Based on Space and Motion

This paper proposes a new bottom-up paradigm for detecting visual saliency in images and videos, which is based on scale space analysis of the log amplitude spectrum of natural images and videos. A salient region is assumed to be any region exhibiting a distinct pattern whose intensity, color, texture and motion is different from the rest of the image. Thus patterns which appear frequently as well as uniform regions are suppressed to produce salient region pop-out. We show that the convolution of the image log amplitude spectrum with a low-pass Gaussian kernel (at the proper scale) is equivalent to such an image saliency detector. A saliency map can then be obtained by reconstructing the 2-D signal using the original phase spectrum and an appropriately filtered log amplitude spectrum to produce pop-out. The optimal scale for each image feature channel (intensity, color, motion) is determined by minimizing the entropy of its saliency map. This produces four maps which are then fused by a weighted linear combination. Significantly, the approach does not require the setting of any parameters. We demonstrate experimentally that the proposed model has the ability to highlight small and large salient regions and to inhibit repeating patterns in both images and videos.

Active Safety in Autonomous Land Vehicle

In this paper, active safety is divided into four stages, which are perception enhancement, driving warning, assistant driving, and autonomous driving. The autonomous driving stage which synthesized all the other stages is emphasized. The relationship of the autonomous driving with intelligent transport systems (ITS) program as well as its development in China is introduced. As an example of autonomous driving system, the prototype HongQi autonomous land vehicle is analyzed systemically. The control subsystem, the environments recognition subsystem and the experiments on the highway of the vehicle are presented in detail. It performs excellently through the thousands kilometers of autonomous driving. The vehicle can be a test-bed for various active safety techniques and parts of the achievements can be commercialized.