This paper presents a hierarchical trajectory planning based on the integration of a sampling and an optimization method for urban autonomous driving. To manage a complex driving environment, the upper behavioral trajectory planner searches the macro-scale trajectory to determine the behavior of an autonomous car by using environment models, such as traffic control device and objects. This planner infers reasonable behavior and provides it to the motion trajectory planner. For planning the behavioral trajectory, the sampling-based approach is used due to its advantage of a free-form cost function for discrete models of the driving environments and simplification of the searching area. The lower motion trajectory planner determines the micro-scale trajectory based on the results of the upper trajectory planning with the environment model. The lower planner strongly considers vehicle dynamics within the planned behavior of the behavioral trajectory. Therefore, the planning space of the lower planner can be limited, allowing for improvement of the efficiency of the numerical optimization of the lower planner to find the best trajectory. For the motion trajectory planning, the numerical optimization is applied due to its advantages of a mathematical model for the continuous elements of the driving environments and low computation to converge minima in the convex function. The proposed algorithms of the sampling-based behavioral and optimization-based motion trajectory were evaluated through experiments in various scenarios of an urban area.

A safe trajectory planning for on-road autonomous driving is a challenging problem owing to the variety and complexity of driving environments. The problem should involve the consideration of numerous aspects such as road geometry, lane-structured roads, traffic regulations, traffic participants, and vehicle physical limitations. Furthermore, dynamically changing movements of surrounding vehicles make the problem more challenging. It requires the planner's ability to react to the changes in the driving environments in real time. To solve this problem, sampling and numerical optimization-based trajectory planners were introduced. However, these methods have their own limitations in generating a safe trajectory in these dynamic scenarios. To overcome these issues, this paper proposes a hybrid trajectory planning scheme to integrate the strength of the sampling and optimization methods. With the sampling method for a lateral movement, the planner can deal with various trajectories with multiple maneuvers. This helps the planner to generate a reactive trajectory in a dynamically changing environment. The numerical optimization of a longitudinal movement enables the planner to adapt to diverse situations without restriction of predefined patterns for specific driving purposes. The proposed method was implemented with an embedded optimization coder and C++ environment. Based on this, its performance was evaluated through simulation and real driving tests in various on-road dynamic scenarios.

Modern vehicles are designed to improve fuel consumption while satisfying emissions regulations. As a result, powertrains are becoming increasingly complex and changing rapidly. Optimal control based on the future vehicle speed is one way to address these changes. In this approach, accurate prediction of velocity is closely related to the performance of optimal control. However, there exists uncertainty and complexity in predicting the driver's behavior, which can be influenced by the surrounding driving environment. In order to overcome such limitations, we propose a prediction algorithm of vehicle speed based on a stochastic model using a Markov chain with speed constraints. The Markov chain, which forms the basis of the proposed algorithm, generates the velocity trajectory stochastically within speed constraints. The constraints are estimated by an empirical model that takes into account the road geometry and is organized by the intuitive form of matrices. To reduce the complexity of the vehicle position and roadway integration, a curvilinear coordinate system is presented using GPS information and a roadway geometry model. Based on these coordinates, the algorithm predicts the future velocity for each cycle from the current vehicle position up to the ahead distance. The proposed algorithm is evaluated through experiments in an urban area, and the test vehicle collects driving data on the roadway position domain. The algorithm was verified in the following conditions: free of surrounding vehicles and traffic lights, the average velocity of the test vehicle is 47.8 km/h, and the maximum velocity of the test vehicle is 66.6 km/h. The experimental results show that a 3.8041 km/h root-mean-square-error is achieved with a prediction horizon of up to 200 m.

High Definition (HD) maps are becoming key elements of the autonomous driving because they can provide information about the surrounding environment of the autonomous car without being affected by the real-time perception limit. To provide the most recent environmental information to the autonomous driving system, the HD map must maintain up-to-date data by updating changes in the real world. This paper presents a simultaneous localization and map change update (SLAMCU) algorithm to detect and update the HD map changes. A Dempster–Shafer evidence theory is applied to infer the HD map changes based on the evaluation of the HD map feature existence. A Rao–Blackwellized particle filter (RBPF) approach is used to concurrently estimate the vehicle position and update the new map state. The detected and updated map changes by the SLAMCU are reported to the HD map database in order to reflect the changes to the HD map and share the changing information with the other autonomous cars. The SLAMCU was evaluated through experiments using the HD map of traffic signs in the real traffic conditions.

Tracking and behavior reasoning of surrounding vehicles on a roadway are keys for the development of automated vehicles and an advanced driver assistance system (ADAS). Based on dynamic information of the surrounding vehicles from the tracking algorithm and driver intentions from the behavior reasoning algorithm, the automated vehicles and ADAS can predict possible collisions and generate safe motion to avoid accidents. This paper presents a unified vehicle tracking and behavior reasoning algorithm that can simultaneously estimate the vehicle dynamic state and driver intentions. The multiple model filter based on various behavior models was used to classify the vehicle behavior and estimate the dynamic state of surrounding vehicles. In addition, roadway geometry constraints were applied to the unified vehicle tracking and behavior reasoning algorithm in order to improve the dynamic state estimation and the behavior classification performance. The curvilinear coordinate system was constructed based on the precise map information in order to apply the roadway geometry to the tracking and behavior reasoning algorithm. The proposed algorithm was verified and evaluated through experiments under various test scenarios. From the experimental results, we concluded that the presented tracking and behavior reasoning algorithm based on the roadway geometry constraints provides sufficient accuracy and reliability for automated vehicles and ADAS applications.