Collision avoidance is one of the most challenging tasks people need to consider for developing the self-driving technology. In this paper we propose a new spatiotemporal motion planning algorithm that efficiently solves a constrained nonlinear optimal control problem using the iterative linear quadratic regulator (iLQR), which takes into account the uncertain driving behaviors of the traffic vehicles and minimizes the collision risks between the self-driving vehicle (referred to as the "ego" vehicle) and the traffic vehicles such that the ego vehicle is able to maintain sufficiently large distances to all the surrounding vehicles for achieving the desired collision avoidance maneuver in traffic. To this end, we introduce the concept of the "collision polygon" for computing the minimum distances between the ego vehicle and the traffic vehicles, and provide two different solutions for designing the constraints of the motion planning problem by properly modeling the behaviors of the traffic vehicles in order to evaluate the collision risk. Finally, the iLQR motion planning algorithm is validated in multiple real-time tasks for collision avoidance using both a simulator and a level-3 autonomous driving test platform.
This paper presents a state and state-input constrained variant of the discrete-time iterative Linear Quadratic Regulator (iLQR) algorithm, with linear time-complexity in the number of time steps. The approach is based on a projection of the control input onto the nullspace of the linearized constraints. We derive a fully constraint-compliant feedforward-feedback control update rule, for which we can solve efficiently with Riccati-style difference equations. We assume that the relative degree of all constraints in the discrete-time system model is equal to one, which often holds for robotics p…
We propose the Selective Densification method for fast motion planning through configuration space. We create a sequence of roadmaps by iteratively adding configurations. We organize these roadmaps into layers and add edges between identical configurations between layers. We find a path using best-first search, guided by our proposed estimate of remaining planning time. This estimate prefers to expand nodes closer to the goal and nodes on sparser layers. We present proofs of the path quality and maximum depth of nodes expanded using our proposed graph and heuristic. We also present experiments…
This paper extends the RRT* algorithm, a recently developed but widely-used sampling-based optimal motion planner, in order to effectively handle nonlinear kinodynamic constraints. Nonlinearity in kinodynamic differential constraints often leads to difficulties in choosing appropriate distance metric and in computing optimized trajectory segments in tree construction. To tackle these two difficulties, this work adopts the affine quadratic regulator-based pseudo metric as the distance measure and utilizes iterative two-point boundary value problem solvers for computing the optimized segments. T…
Optimal control is often used in robotics for planning a trajectory to achieve some desired behavior, as expressed by the cost function. Most works in optimal control focus on finding a single optimal trajectory, which is then typically tracked by another controller. In this work, we instead consider trajectory distribution as the solution of an optimal control problem, resulting in better tracking performance and a more stable controller. A Gaussian distribution is first obtained from an iterative Linear Quadratic Regulator (iLQR) solver. A short horizon Model Predictive Control (MPC) is then…
i2LQR: Iterative LQR for Iterative Tasks in Dynamic Environments
University of California, Berkeley（加州大学伯克利分校）；Xi’an Jiaotong University（西安交通大学）；Shanghai Jiao Tong University（上海交通大学）；University of Minnesota-Twin Cities（明尼苏达大学双城分校）
Real-time motion planning is a vital function of robotic systems. Different from existing roadmap algorithms which first determine the free space and then determine the collision-free path, researchers recently proposed several convex relaxation based smoothing algorithms which first select an initial path to link the starting configuration and the goal configuration and then reshape this path to meet other requirements (e.g., collision-free conditions) by using convex relaxation. However, convex relaxation based smoothing algorithms often fail to give a satisfactory path, since the initial pa…
The Hong Kong University of Science and Technology（香港科技大学）
Developments in cooperative trajectory planning of connected autonomous vehicles (CAVs) have gathered considerable momentum and research attention. Generally, such problems present strong non-linearity and non-convexity, rendering great difficulties in finding the optimal solution. Existing methods typically suffer from low computational efficiency, and this hinders the appropriate applications in large-scale scenarios involving an increasing number of vehicles. To tackle this problem, we propose a novel decentralized iterative linear quadratic regulator (iLQR) algorithm by leveraging the dual…
Many problems in robotics involve multiple decision making agents. To operate efficiently in such settings, a robot must reason about the impact of its decisions on the behavior of other agents. Differential games offer an expressive theoretical framework for formulating these types of multi-agent problems. Unfortunately, most numerical solution techniques scale poorly with state dimension and are rarely used in real-time applications. For this reason, it is common to predict the future decisions of other agents and solve the resulting decoupled, i.e., single-agent, optimal control problem. Th…
Iterative learning control has been successfully used for several decades to improve the performance of control systems that perform a single repeated task. Using information from prior control executions, learning controllers gradually determine open-loop control inputs whose reference tracking performance can exceed that of traditional feedback-feedforward control algorithms. This paper considers iterative learning control for a previously unexplored field: autonomous racing. Racecars are driven multiple laps around the same sequence of turns while operating near the physical limits of tire-…
Rio Tinto Centre for Mining Automation（力拓矿业自动化中心）；Australian Centre for Field Robotics（澳大利亚野外机器人中心）；Faculty of Engineering and IT, University of Sydney（悉尼大学工程与信息技术学院）
This paper addresses the problem of planning time-optimal trajectories for multiple cooperative agents along specified paths through a static road network. Vehicle interactions at intersections create non-trivial decisions, with complex flow-on effects for subsequent interactions. A globally optimal, minimum time trajectory is found for all vehicles using Mixed Integer Linear Programming (MILP). Computational performance is improved by minimising binary variables using iteratively applied targeted collision constraints, and efficient goal constraints. Simulation results in an open-pit mining s…
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）
Detection of road curbs is an essential capability for autonomous driving. It can be used for autonomous vehicles to determine drivable areas on roads. Usually, road curbs are detected on-line using vehicle-mounted sensors, such as video cameras and 3-D Lidars. However, on-line detection using video cameras may suffer from challenging illumination conditions, and Lidar-based approaches may be difficult to detect far-away road curbs due to the sparsity issue of point clouds. In recent years, aerial images are becoming more and more worldwide available. We find that the visual appearances betwee…
Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
Technical University of Munich（慕尼黑工业大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；University College London（伦敦大学学院）
A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical …
Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis
Fraunhofer IVI（弗劳恩霍夫应用信息学研究所（IVI））；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Technical University of Munich（慕尼黑工业大学）；Fraunhofer IOSB（弗劳恩霍夫光学、电子和图像利用研究所（IOSB））
IteraOptiRacing: A Unified Planning-Control Framework for Real-time Autonomous Racing for Iterative Optimal Performance
Shanghai Jiao Tong University（上海交通大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Minnesota-Twin Cities（明尼苏达大学-双城分校）；University of California, Berkeley（加州大学伯克利分校）
Safety Perspective on Assisted Lane Changes: Insights from Open-Road, Live-Traffic Experiments
European Commission, Joint Research Centre（欧洲委员会联合研究中心）；Vanderbilt University（范德堡大学）；European Commission, Directorate General for Internal Market, Industry, Entrepreneurship and SMEs（欧洲委员会内部市场、工业、创业和中小企业总司）
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术研究所）
We present an approach for reconstructing vehicles from a single (RGB) image, in the context of autonomous driving. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in \emph{shape priors}, which are learnt over a small keypoint-annotated dataset. We then formulate a shape-aware adjustment problem that uses the learnt shape priors to recover the 3D pose and shape of a query object from an image…
SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification
Fariborz Maseeh Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校土木、建筑与环境工程学院）；School of Architecture, The University of Texas at Austin（德克萨斯大学奥斯汀分校建筑学院）
