Recently, the research of performance prediction with prior knowledge obtained by machine learning (ML) techniques has been widely studied. In this paper, we present the first work of machine learning framework using complex network features to predict wire-length in physical design. The experimental result on TAU 2017 Benchmark shows the effectiveness and efficiency of our method. The predictors based on four machine learning models provide a high accuracy and reasonable speed compared with normal EDA (Electronic Design Automation) tool.

This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with each obstacle at every moment throughout the entire parking process. With a coarse trajectory guiding a homotopic route, the intractably scaled collision-avoidance constraints are replaced by within-corridor constraints, whose scale is small and independent from the environment complexity. Constructing such a corridor sacrifices partial free spaces, which may cause loss of optimality or even feasibility. To address this issue, our proposed method reconstructs the corridor in an iterative framework, where a lightweight OCP with only box constraints is quickly solved in each iteration. The proposed planner, together with several prevalent optimization-based planners are tested under 115 simulation cases w.r.t. the success rate and computational time. Real-world indoor experiments are conducted as well.

The control module is a crucial part for autonomous driving systems, a typical control algorithm often requires vehicle dynamics (such as longitudinal dynamics) as inputs, which, unfortunately are difficult to calibrate in real time. Further, it is also a challenge to reflect instantaneous changes in longitudinal dynamics (e.g. load changes) using a calibration table. As a result, control performance may deteriorate when load changes considerably (especially for small cargoes). In this paper, we will show how we build a data-driven longitudinal calibration procedure using machine learning techniques to adapt load changes in real time. We first generated offline calibration tables from human driving data. The offline table serves as an initial guess for later uses, and it only requires twenty minutes of data collection and processing. We then used an online learning algorithm to appropriately update the initial table (the offline table) based on real-time performance analysis. Experiments indicated (a) offline auto-calibration leads to a better control accuracy, compared with manual calibration; (b) online auto-calibration is capable to handle load changes and significantly reduce real time control error. This system has been deployed to more than one hundred Baidu self-driving vehicles (both hybrid and electronic vehicles) since April 2018. By January 2019, the system had been tested for more than 2,000 hours and over 10,000 kilometers (6,213 miles) and was still proven to be effective.

Trajectory planning for a tractor-trailer vehicle is challenging because the vehicle kinematics consists of underactuated and nonholonomic constraints that are highly coupled. Prevalent sampling-based or search-based planners suitable for rigid-body vehicles are not capable of handling the tractor-trailer vehicle cases. This work aims to deal with generic n-trailer cases in the tiny environments. To this end, an optimal control problem is formulated, which is beneficial in being accurate, straightforward, and unified. An adaptively homotopic warm-starting approach is proposed to facilitate the numerical solution process of the formulated optimal control problem. Compared with the existing sequential warm starting strategies, our proposal can adaptively define the subproblems with the purpose of making the gaps between adjacent subproblems “pleasant” for the solver. Unification and efficiency of the proposed adaptively homotopic warm-starting approach have been investigated in several extremely tiny scenarios. Our planner finds solutions that other existing planners cannot. Online planning opportunities are briefly discussed as well.

To diagnose the patella dislocation, various angles and distances need to be measured on knee CT images, which was traditionally done by doctors manually. In this work, we propose a novel scheme for automatic measurement on knee CT images to assist doctors diagnosis of patella dislocation. Specifically, we first segment the femur and the patella regions on the CT images, then adopt optimal fitting to obtain the central planes of the femur and the patella bones and, based on which, make the measurement. As experimentally demonstrated, the measured results obtained with our system are highly consistent with those manually made by experienced doctors.