Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California

Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017

Implicit Cooperative Positioning in Vehicular Networks

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.Comment: 15 pages, 10 figures, in review, 201

RadarSLAM: Radar based Large-Scale SLAM in All Weathers

Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall

Cross Hallway Detection and Indoor Localization Using Flash Laser Detection and Ranging

A flash LADAR is investigated as a source of navigation information to support cross-hallway detection and relative localization. To accomplish this, a dynamic, flexible simulation was developed that simulated the LADAR and the noise of a LADAR system. Using simulated LADAR data, algorithms were developed that were shown to be effective at detecting cross hallways in simulated ideal environments and in simulated environments with noise. Relative position was determined in the same situations. A SwissRanger SR4000 flash LADAR was then used to collect real data and to verify algorithm performance in real environments. Hallway detection was shown to be possible in all real data sets, and the relative position-finding algorithm was shown to be accurate when compared to the absolute accuracy of the LADAR. Thus, flash LADAR is concluded to be an effective source for indoor navigation information

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

A Novel Localization System for Experimental Autonomous Underwater Vehicles

Localization is a classic and complex problem in the field of mobile robotics. It becomes particularly challenging in an aqueous environment because currents within the water can move the robot. A novel localization module and corresponding localization algorithm for experimental autonomous underwater vehicles is presented. Unlike other available positioning systems which require fixed hardware beacons, this custom built module relies only on information available from sensors on-board the vehicle and knowledge of its bounded domain. This allows the user to save valuable time which would otherwise be devoted to the setup and calibration of a beacon or sensor network. The module uses three orthogonal ultrasonic transducers to measure distances to the tank boundaries. Using the measured tri-axial orientation of the vehicle, the algorithm analytically determines the robot\u27s position within the domain in absolute coordinates. Certain vehicle states do not allow the position to be completely resolved by the algorithm alone. In this case, state estimation is used to estimate the robot position until its state is no longer indeterminate. The modular design of this system makes it ideal for application on underwater vehicles which operate in a bounded environment for research purposes. An experimental version of the module was constructed and tested in the WPI swimming pool and showed successful localization under normal conditions

Landmine Detection Rover

The goal of this project is to create an adaptable landmine detection platform to allow for autonomous marking and detonation of PMN-1 anti-personnel landmines without the need of endangering personnel and to make this landmine disposal operation economically feasible for poor regions of the world. This project is intended to be an adaptable prototype to be built upon by future teams. This project produced a prototype landmine detection and marking robot that utilized GPS localization, autonomous navigation and mapping, a prototype metal detection system and novel landmine marking system