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

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

Safe handling of dynamic highway and inner city scenarios with autonomous vehicles involves the problem of generating traffic-adapted trajectories. In order to account for the practical requirements of the holistic autonomous system, we propose a semi-reactive trajectory generation method, which can be tightly integrated into the behavioral layer. The method realizes long-term objectives such as velocity keeping, merging, following, stopping, in combination with a reactive collision avoidance by means of optimal-control strategies within the Frenet-Frame [12] of the street. The capabilities of this approach are demonstrated in the simulation of a typical high-speed highway scenario.

Optimal trajectory generation for dynamic street scenarios in a Frenét Frame

This paper presents a generic approach for tactical decision-making under uncertainty in the context of driving. The complexity of this task mainly stems from the fact that rational decision-making in this context must consider several sources of uncertainty: The temporal evolution of situations cannot be predicted without uncertainty because other road users behave stochastically and their goals and plans cannot be measured. Even more important, road users are only able to perceive a tiny part of the current situation with their sensors because measurements are noisy and most of the environment is occluded. In order to anticipate the consequences of decisions a probabilistic approach, considering both forms of uncertainty, is necessary. The authors address this by formulating the task of driving as a continuous Partially Observable Markov Decision Process (POMDP) that can be automatically optimized for different scenarios. As driving is a continuous-space problem, the belief space is infinite-dimensional. The authors do not use a symbolic representation or discretize the state space a priori because there is no representation of the state space that is optimal for every situation. Instead, they employ a continuous POMDP solver that learns a good representation of the specific situation.

Probabilistic Decision-Making under Uncertainty for Autonomous Driving Using Continuous POMDPs

This paper deals with the trajectory generation problem faced by an autonomous vehicle in moving traffic. Being given the predicted motion of the traffic flow, the proposed semi-reactive planning strategy realizes all required long-term maneuver tasks (lane-changing, merging, distance-keeping, velocity-keeping, precise stopping, etc.) while providing short-term collision avoidance. The key to comfortable, human-like as well as physically feasible trajectories is the combined optimization of the lateral and longitudinal movements in street-relative coordinates with carefully chosen cost functionals and terminal state sets (manifolds). The performance of the approach is demonstrated in simulated traffic scenarios.

Optimal trajectories for time-critical street scenarios using discretized terminal manifolds

This paper reports on the behaviour decision and execution unit of AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the DARPA Urban Challenge 2007 competition. Starting with a short description of the car and its major hardware components, we outline the underlying software structure and focus on the design of the behavior decision module. The selection of maneuvers necessary to accomplish the mission is conducted online via a concurrent hierarchical state machine that specifically ascertains behavior in accordance with California traffic rules. The states and transitions used to model a adequate behaviour are described. We conclude with a report of the results achieved during the competition.

Design of the planner of team AnnieWAY’s autonomous vehicle used in the DARPA Urban Challenge 2007

This paper describes an algorithm for handling moving traffic which was deployed on AnnieWAY, an autonomous vehicle successfully entering the finals of the DARPA Urban Challenge 2007 competition. The algorithm allows for a robust and effective collision check for a variety of maneuvers including turning at intersections with oncoming traffic, merging into moving traffic, changing lanes, as well as dynamic passing and can easily be integrated into a high-level decision process, such as a state machine.

A robust algorithm for handling moving traffic in urban scenarios

This paper addresses the problem of path planning for a non-holonomic vehicle in fast changing urban environments. The developed method is flexible in utilization and generates collision-free paths with continuous curvature for a wide range of maneuvers such as regular lane-driving, rectangular or parallel parking and K-Turns. An A* algorithm is used as incremental planning method in combination with a kinematic model to follow non-holonomic constraints. A shortest-path estimate is used as optimization criteria. In the context of collision-free driving, the idea of using risk maps for representation of obstacles and other environmental influences is presented. Simulation and experiments show the applicability of this approach for lane driving as well as narrow navigation in parking spots. Further ideas of enhancements are discussed in the outlook of the paper.

Path planning for cognitive vehicles using risk maps

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the DARPA Urban Challenge 2007 competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. A recent laser scanner plays the prominent role in the perception of the environment. It measures range and reflectivity for each pixel. While the former is used to provide 3D scene geometry, the latter allows robust lane marker detection. Mission and maneuver selection is conducted via a concurrent hierarchical state machine that specifically ascertains behavior in accordance with California traffic rules. We conclude with a report of the results achieved during the competition.

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Daniel Jagszent

Papers

Design of the planner of team AnnieWAY’s autonomous vehicle used in the DARPA Urban Challenge 2007

A robust algorithm for handling moving traffic in urban scenarios

Path planning for cognitive vehicles using risk maps

Team AnnieWAY's autonomous system

An approach for behavior selection in an autonomous vehicle