Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side by side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

A Review of Motion Planning Techniques for Automated Vehicles

Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side-by-side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Summary

This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing. Copyright © 2014 John Wiley & Sons, Ltd.

Optimization‐based autonomous racing of 1:43 scale RC cars

http://manuscript.elsevier.com/S0005109822000735/pdf/S0005109822000735.pdf

Decentralized route-planning for multi-vehicle teams to satisfy a subclass of linear temporal logic specifications

Motion-planning for autonomous vehicles involves a two-level planning hierarchy: a high-level task-planning algorithm and a lower-level trajectory generation algorithm. The task planner operates on a discrete structure, such as a graph, whereas the trajectory generator operates on a dynamical system with a continuous state space. The problem of ensuring “compatibility” between these two planners has been approached in the literature by constructing discrete abstractions of continuous systems. However, such abstractions do not always exist, especially for nonholonomic vehicle dynamical models. We propose abstractions for such models based on geometric analysis of the vehicle's motion. The proposed motion-planning approach ensures that the task planner operates independently of the trajectory generation algorithm while maintaining a guarantee of “compatibility”, and also provides significant reductions in overall execution time.

https://users.wpi.edu/~rvcowlagi/publications/2014-acc-abstractions.pdf

Geometric abstractions of vehicle dynamical models for intelligent autonomous motion

The envisioned scope of autonomy of Unmanned Aerial Vehicles (UAVs) has broadened from autonomous point-to-point motion to intelligent motion, which involves the completion of a high-level task. Algorithmic frameworks for defining and completing such tasks have been developed in the theory of artificial intelligence (AI). One such framework is of classical planning problems (CPP)s. We discuss the applicability of CPPs to formulate new types of complex UAV tasks. Next, we present a new technique to unify trajectory optimization algorithms with solutions of CPPs, thereby introducing a new class of UAV guidance techniques. The proposed approach is particularly suited to leverage existing algorithms for solving CPPs and, independently, algorithms for trajectory optimization. We introduce a family of graphs called lifted planning graphs parametrized by an integer H, and we map paths in these graphs to solutions of the CPP. We show that the overall cost of a high-level plan is a nonincreasing function of H, and that there exists a finite H for which an optimal path in the lifted planning graph is associated with the optimal solution of the CPP. We illustrate the proposed ideas with numerical simulation examples involving task-planning for a point-mass UAV model.

Unifying Artificial Intelligence and Trajectory Optimization for UAV Guidance

Stabilization of Helicopter Sling Loads with Passive and Active Control Surfaces

In this paper, we present a novel approach for mitigating positioning errors in vehicle-to-vehicle (V2V) networking environments where digital beamforming is conducted at both transmitters and receivers. Location information of all transmitters and receivers in V2V networks performing beamforming is essential in order to ensure reliable link quality. However, there exists several sources of error where this location information can be corrupted or out-of-date. By leveraging the proposed approach in this paper employing state estimation, these errors can be mitigated, thus providing more accurate location information relative to V2V networking architectures that do not employ techniques to help mitigate potential sources of location error. Simulation results show a $99\%$ improvement of the proposed approach relative to V2V beamformiong architectures that do not account for location errors.

https://par.nsf.gov/servlets/purl/10091706

Raghvendra V. Cowlagi

Papers

Decentralized route-planning for multi-vehicle teams to satisfy a subclass of linear temporal logic specifications

Geometric abstractions of vehicle dynamical models for intelligent autonomous motion

Unifying Artificial Intelligence and Trajectory Optimization for UAV Guidance

Stabilization of Helicopter Sling Loads with Passive and Active Control Surfaces

State Estimation for Mitigating Positioning Errors in V2V Networks Employing Dual Beamforming