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

An autonomous controller for a vehicle. The controller has a processor configured to receive position signals from position sensors and to generate operation control signals defining an updated travel path for the vehicle. The controller has a programmable interface providing communication among the position sensors, the operation control mechanisms, and the processor. The controller is configured to normalize inputs to the processor from the position sensors and to generate compatible operation control signals applied as the inputs to the operation control mechanisms. The processor and the programmable interface define a self-contained unit configurable for operation with a variety of different remote sensors and different remote operation control mechanisms.

Control and systems for autonomously driven vehicles

Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This monograph attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.

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Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

In this paper, we present an algorithm for generating complex dynamically feasible maneuvers for autonomous vehicles traveling at high speeds over large distances. Our approach is based on performing anytime incremental search on a multi-resolution, dynamically feasible lattice state space. The resulting planner provides real-time performance and guarantees on and control of the suboptimality of its solution. We provide theoretical properties and experimental results from an implementation on an autonomous passenger vehicle that competed in, and won, the Urban Challenge competition.

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Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles

This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system–denied and highly dynamic environments with poor a priori information. © 2008 Wiley Periodicals, Inc.

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A perception-driven autonomous urban vehicle

Stereo vision system for a vehicle, the system receiving input from various combinations of at least three cameras depending on the status of the vehicle (e.g., speed, steering angle, and so forth) or depending on the surrounding environment (e.g., the rate that obstacles are detected, the size of the obstacles, and so forth). The three cameras are mounted asymmetrically spaced apart from each other, so that the baseline between each combination of two cameras is different. The vision system uses a combination of the cameras having >a wide baseline in order to detect obstacles that are relatively far away and a combination of cameras having a shorter baseline to detect obstacles that are closer to the cameras.

Vision system for an autonomous vehicle

A system and method for controlling a convoy of vehicles. The convoy of vehicles including a leader vehicle in communication with a plurality of autonomous follower vehicles. The leader vehicle is configured to receive a first autonomous follower vehicle data and compare the first autonomous follower vehicle data to at least one of a leader vehicle data, a second autonomous follower vehicle data and/or a threshold value. The first autonomous follower vehicle data, the leader vehicle data, the second autonomous follower vehicle data and the threshold value relate to a vehicle performance characteristic.

Vehicle diagnostics based on information communicated between vehicles

A vehicle control system includes a controller configured to provide an output signal in response to an input signal. The output signal is used to control a vehicle operating parameter. The vehicle control system also includes a simulation module in communication with the controller and configured to execute a vehicle dynamics model to simulate a vehicle response based on the input signal and the vehicle operating parameter. The controller is configured to provide the output signal based on the simulated vehicle response.

Vehicle control system and method

The TerraMax vehicle is based on Oshkosh Truck’s Medium Tactical Vehicle Replacement (MTVR) truck platform and was one of the 5 vehicles able to successfully reach the finish line of the 132 miles DARPA Grand Challenge desert race. Due to its size (30,000 pounds, 27′-0″ long, 8′-4″ wide, and 8′-7″ high) and the narrow passages, TerraMax had to travel slowly, but its capabilities demonstrated the maturity of the overall system. Rockwell Collins developed, integrated, and installed the intelligent Vehicle Management System (iVMS), which includes vehicle sensor management, navigation, and vehicle control systems. The University of Parma provided the vehicle’s vision system, while Oshkosh Truck Corp. provided project management, system integration, low level controls hardware, modeling and simulation support and the vehicle.

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The TerraMax Autonomous Vehicle

A self-contained axle module for a vehicle. The vehicle includes a support structure, a source for electric power, a source of cooling medium, and a vehicle control device. The self-contained axle includes a first side plate removably coupled to the support structure. A second side plate removably coupled to the support structure. A housing coupled to the first and second side plate. An electric motor coupled to the housing and the source for electric power. A motor drive controller unit coupled to the electric motor and to the vehicle control device to communicate signals to the vehicle control device such that one of the speed and torque of the electric motor is controlled based upon the signals. A gear train is mounted in the housing and coupled to the electric motor and an output shaft, wherein, the self-contained axle module can be selectively coupled and decoupled from the support structure.

Gary Schmiedel

Papers

Vision system for an autonomous vehicle

Vehicle diagnostics based on information communicated between vehicles

Vehicle control system and method

The TerraMax Autonomous Vehicle

Self-contained axle module