This paper presents a novel approach to autonomous driving at the vehicle's handling limits. Such a system requires a high speed, consistent control signal as well as numerous safety features capable of monitoring and stopping the vehicle. When operating, the system's high level controller utilizes a highly accurate differential GPS and known friction values to drive a precomputed path at the friction limits of the vehicle. The system was tested in a variety of road conditions, including the challenging Pikes Peak Hill climb. Results from this work can be extended to improve driving safety and accident avoidance in vehicles.

This paper presents a novel approach to segmentation of a dense 3D point cloud, generated by a flash lidar type camera. Incorporating symmetries of the sensor, the algorithm is using a 2D grid approach to cluster data points and extrude object segments in complex scenes. The data representation allows for the handling of partially occluded, but connected objects at different ranges. The algorithm was tested on a variety of different sensor data sets and the obtained results are presented and discussed.

In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential. We previously published an overview of Junior, Stanford's entry in the 2007 DARPA Urban Challenge. This race was a closed-course competition which, while historic and inciting much progress in the field, was not fully representative of the situations that exist in the real world. In this paper, we present a summary of our recent research towards the goal of enabling safe and robust autonomous operation in more realistic situations. First, a trio of unsupervised algorithms automatically calibrates our 64-beam rotating LIDAR with accuracy superior to tedious hand measurements. We then generate high-resolution maps of the environment which are subsequently used for online localization with centimeter accuracy. Improved perception and recognition algorithms now enable Junior to track and classify obstacles as cyclists, pedestrians, and vehicles; traffic lights are detected as well. A new planning system uses this incoming data to generate thousands of candidate trajectories per second, choosing the optimal path dynamically. The improved controller continuously selects throttle, brake, and steering actuations that maximize comfort and minimize trajectory error. All of these algorithms work in sun or rain and during the day or night. With these systems operating together, Junior has successfully logged hundreds of miles of autonomous operation in a variety of real-life conditions.

Advanced Driver Assistance Systems (ADAS) are becoming more common in today's vehicles with more complex functions being implemented in production vehicles. On the research side, significant technology development in hardware and software has been spurred in recent years by the DARPA Grand Challenge and Urban Challenge in 2005 and 2007 respectively. This paper describes `Junior 3', a vehicle built at Volkswagen Group of America's Electronics Research Lab, based on the knowledge gained from the DARPA Challenges and applied to ADAS functionalities. Specifically we focussed on object detection and vehicle positioning capabilities with close to production grade sensors and describe an `Autonomous Valet Parking' and `Object Tracking' application with this system.

In order to establish mobile robot operations and to realize survey and inspection tasks, robust and precise measurements of the geometry of the 3-D environment is a required basic sensor technology. For visual inspection, surface classification, and documentation purposes, however additional information concerning reflectance of measured objects is necessary. High-speed acquisition of both geometric and visual information is achieved by the described active laser radar developed at Zoller+Frohlich (Z+F). In contrast to other range sensing devices, the Z+F system is designed for high-speed and high-performance operation in real indoor and outdoor environments, emitting a minimum of near-infrared laser energy. It integrates a single-point laser measurement system and a mechanical deflection system for 3D environmental measurements. Experimental results are reported from surface inspections in tunnels, the generation of 3D CAD models of a work cell in an automotive manufacturing plant and the modeling of free form surfaces such as historic sculptures.

Laser scanners, or laser radars (ladar), have been used for a number of years for mobile robot navigation and inspection tasks. Although previous scanners were sufficient for low speed applications, they often did not have the range or angular resolution necessary for mapping at the long distances. Many also did not provide an ample field of view with high accuracy and high precision. In this paper we will present the development of state-of-the-art, high speed, high accuracy, 3D laser radar technology. This work has been a joint effort between CMU and K2T and Z+F. The scanner mechanism provides an unobstructed 360/spl deg/ horizontal field of view, and a 70/spl deg/ vertical field of view. Resolution of the scanner is variable with a maximum resolution of approximately 0.06 degrees per pixel in both azimuth and elevation. The laser is amplitude-modulated, continuous-wave with an ambiguity interval of 52 m, a range resolution of 1.6 mm, and a maximum pixel rate of 625 kHz. This paper will focus on the design and performance of the laser radar and will discuss several potential applications for the technology. It reports on performance data of the system including noise, drift over time, precision, and accuracy with measurements. Influences of ambient light, surface material of the target and ambient temperature for range accuracy are discussed. Example data of applications will be shown and improvements will also be discussed.

The design of a MMW radar sensor for mobile robot outdoor navigation is presented, which is able to operate robustly even under adverse weather conditions. This sensor has a range of approximately 200 metres and uses a linear array of receivers and wavefront reconstruction techniques to compute range and bearing of objects within the field of view. Currently sensor design and hardware fabrication have been completed and the sensor is being integrated with other vehicle systems. Signal processing techniques are discussed and results obtained from simulation and the real sensor are presented.

In this paper, we report on experiments with a core system for autonomous navigation in outdoor natural terrain. The system consists of three parts: a perception module which processes range images to identify untraversable regions of the terrain, a local map management module which maintains a representation of the environment in the vicinity of the vehicle, and a planning module which issues commands to the vehicle controller. Our approach uses reactive planning for generating commands to drive the vehicle along with "early traversability evaluation," in which the perception module decides which parts of the terrain are traversable as soon as a new image is taken. We argue that our approach leads to a robust and efficient navigation system. We illustrate our approach by an experiment in which a vehicle travelled autonomously for one kilometer through unmapped cross-country terrain.>

We describe a core system for autonomous navigation in outdoor natural terrain. The system consists of three parts: a perception module that processes range images to identify untraversable regions of the terrain, a local map management module that maintains a representation of the environment in the vicinity of the vehicle, and a planning module that issues commands to the vehicle controller. We illustrate our approach by an experiment in which a vehicle travelled autonomously for one kilometer through unmapped cross-country terrain.>

collisions Is an Important task for any autonomous mobile system. Thls paper describes an apprvach uslng a sonar system that we Implemented for the autonomous land vehlcle Navlab. The general hardware configuration of the system ls shown, followed by a description of how the system buikb a local grid map of its envlronment. The Information collected In the map can then be used for a varkty of a plications in vehlcle navigation Uke colllslon avoidance, gaturn tracking and parking. A slmple algorithm was Implemented that can track a static feature such as a rail, wall or an array of parked cars and use this Information to drlve the vehlcle. Methods for Illterlng the raw data and generatlng the steerlng commands BIP discussed and the Implementation for collision avoidance and

Deriving a terrain model from sensor data is an important task for the autonomous navigation of a mobile robot. An approach is presented for autonomous underwater vehicles using a side scan sonar system. Some general aspects of the type of data and filtering techniques to improve it are discussed. An estimated bottom contour is derived using a geometric reflection model and information about shadows and highlights. Several techniques of surface reconstruction and their limitations are presented. A method is presented for feature extraction which is important for future data matching/fusion procedures.>