Search

Highly automated driving requires the use of multiple sensors for reliable tracking functionality. In response to the requirement, the proposed method modifies the conventional Interacting Multiple Model (IMM) filter to fuse multi-sensor data by utilizing the independence of observations. In addition, the proposed IMM is integrated with a Centralized Kalman Filter (CKF) that ensures track continuity against sensor failures, providing optimal state estimates. When tracking objects in a moving reference frame, such as in autonomous vehicles, onboard sensor measurements represent relative values, making it challenging to estimate the actual motion of objects. While transforming states to a global coordinates is a solution, the solution can arise another problem where the tracking results depends on the status and performance of the localization. Therefore, to tackle the problem, a track compensation algorithm utilizing a hybrid coordinate system is proposed. The actual motions of objects are estimated based on errors between the track state and the associated measurement. The performance of the proposed algorithms is demonstrated using experimental scenario data conducted with an actual vehicle.

Precise localization is essential for reliable autonomous driving. Traditionally, many systems have turned to lane level map matching techniques utilizing High Definition Maps (HD-Maps). However, it is necessary to have considerate efforts tied to the continuous availability and timeliness of these HD-Maps. Taking this into account, this paper explores an alternative approach, focusing on landmark-based odometry and a filtering-based technique. The proposed localization algorithm is integrated with the GPS and the odometry in a loosely coupled approach. Because it is important to obtain an initial pose in order to utilize the odometry, the initial odometry rotation is estimated from the filtering method, as an alternative to optimization methods, to improve the global consistency in localization. In addition, because specific landmark-based odometry, like lidar odometry, can become vulnerable in geometrically repetitive scenarios such as in tunnels, this study considers the change in vehicle speed and updates the landmark-based odometry, aiming to address the limitations. Through experimental tests in normal scenarios and challenging scenarios like tunnels, it is demonstrated that our method offers certain benefits over the existing techniques.

This paper proposes a behavior planning algorithm for self-driving vehicles to handle lane keeping, speed control considering inter-vehicle space, and collision avoidance under uncertainty. The behavior planning approach is structured as a hierarchically organized Markov Decision Process (MDP) comprising two components: the path planning MDP and the speed profiling MDP. The path planning MDP generates multiple path candidates using lane-change path data collected from human drivers. Evaluation of each path candidate is based on reward and penalty terms. The path planning MDP spans and updates considering the predicted simulation time of the path candidate. Subsequently, the speed profiling MDP determines the optimal sequence of speeds for the host vehicle on the planned path. Evaluation of the path and speed profile utility is performed using reward and penalty terms based on the current states of vehicles and road structure. A unique aspect of this approach is the incorporation of uncertainty-aware collision risk and interaction-aware gap penalty, which account for the uncertainty of perception and traffic motion. Various cut-in scenarios are presented in simulations to demonstrate the effectiveness of the proposed algorithm.

The tracking accuracy of nearby vehicles determines the safety and feasibility of driver assistance systems or autonomous vehicles. Recent research has been active to employ additional sensors or to combine heterogeneous sensors for more accurate tracking performance. Especially, autonomous driving technologies require a sensor fusion technique that considers various driving environments. In this research, a novel method for high-level data fusion is proposed to improve the accuracy of tracking surrounding vehicles. In response to the changing driving environment, the locations of the vehicles are estimated in real-time using an adaptive track-to-track fusion technique and an interacting multiple model filter. Asynchronous measurements from multiple sensors such as radar, camera, and LiDAR, are utilized for the estimation. For each sensor, two motion models representing the vehicle’s movement are applied to increase the estimation accuracy. Utilizing a multimodal network-based track-to-track fusion approach, it combines the estimates of the target vehicle position from each sensor into a single estimate. The inputs of the network are intended to determine the reliability of each sensor, considering the driving conditions that may affect sensor accuracy. Also, multiple embeddings in the network are created so that the corresponding data maintains its relevance and enables the real-time computing. The proposed method is verified using real driving data collected from various environments.

A robust and high-performance lateral control system is necessary for autonomous bus driving in complex downtown roads. In this paper, a Sliding Mode (SM) controller with an uncertainty observer is proposed to reduce the chattering phenomenon while ensuring robustness and maintaining the control performance. A stiffness gain is introduced in the cornering stiffness of rear wheels to compensate for the model uncertainty and external disturbance. For lane keeping and changing maneuvers, an optimal path planning algorithm is designed by considering dynamic constraints of the bus. The lane departure of bus is prevented with minimizing the lateral jerk through the sampling-based path regeneration process. The proposed lateral control system is compared to other conventional SM based controllers in the TRUCKSIM simulator. In an experiment with a real bus, lane keeping maneuver is conducted and compared with experienced human driver in the same driving course. Lane change maneuver is also tested with real bus to perform smooth lane transition while satisfying the lateral acceleration and yaw rate limitations.

This paper presents an efficient hierarchical motion planning framework with a long planning horizon for autonomous driving in structured environments. A 3D motion planning with time information is a non-convex problem because there exists more than one local minimum point and various constraints such as roads and obstacles. Accordingly, to deal with high computational complexity and the problem of falling into a local minimum in an enormous solution space, a decoupled method is utilized, that consists of two steps: Long-term planning and short-term planning. First, the long-term planner provides reasonable far-sighted behavior through two processes. In the first process, a rough path that includes a driving strategy is generated in the 2D spatial space. Then, the jump point search algorithm is utilized with time information on the path to reduce the computational burden of A*, giving an acceptable quality of solution at the same time. In this step, a safe, comfortable, and dynamically feasible trajectory is generated. Next, the short-term planner optimizes a short-sighted trajectory using particle swarm optimization. In this method, a steering angle set is encoded as a particle, resulting in a safe, comfortable, and kinodynamically feasible trajectory. The proposed algorithm is implemented and evaluated in a range of vehicle-in-the-loop simulation scenarios, which include various virtual static and dynamic obstacles generated by Intelligent Driver Model. In the evaluation results, the proposed method reduced the computation time by up to 0.696 s with increasing the step cost by up to about 3%. The proposed algorithm is executed every 100 ms for a planning horizon of 10 seconds, and the average computation time is 31 ms with the worst-case computation time of 94 ms.