7 research outputs found
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
For human drivers, having rear and side-view mirrors is vital for safe
driving. They deliver a more complete view of what is happening around the car.
Human drivers also heavily exploit their mental map for navigation.
Nonetheless, several methods have been published that learn driving models with
only a front-facing camera and without a route planner. This lack of
information renders the self-driving task quite intractable. We investigate the
problem in a more realistic setting, which consists of a surround-view camera
system with eight cameras, a route planner, and a CAN bus reader. In
particular, we develop a sensor setup that provides data for a 360-degree view
of the area surrounding the vehicle, the driving route to the destination, and
low-level driving maneuvers (e.g. steering angle and speed) by human drivers.
With such a sensor setup we collect a new driving dataset, covering diverse
driving scenarios and varying weather/illumination conditions. Finally, we
learn a novel driving model by integrating information from the surround-view
cameras and the route planner. Two route planners are exploited: 1) by
representing the planned routes on OpenStreetMap as a stack of GPS coordinates,
and 2) by rendering the planned routes on TomTom Go Mobile and recording the
progression into a video. Our experiments show that: 1) 360-degree
surround-view cameras help avoid failures made with a single front-view camera,
in particular for city driving and intersection scenarios; and 2) route
planners help the driving task significantly, especially for steering angle
prediction.Comment: to be published at ECCV 201
Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications
USDOT Grant 69A3551747114Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV\u2019s) is near at hand due to advances in sensor and computing technology. These capabilities in conjunction with high-definition maps enable lane determination, per lane queue determination, and enhanced performance in applications. This project investigated, analyzed, and demonstrated these related technologies. Project contributions include: (1) Experimental analysis demonstrating that the USDOT Mapping tool achieves internal horizontal accuracy better than 0.2 meters (standard deviation); (2) Theoretical analysis of lane determination accuracy as a function of both distance from the lane centerline and positioning accuracy; (3) Experimental demonstration and analysis of lane determination along the Riverside Innovation Corridor showing that for a vehicle driven within 0.9 meters of the lane centerline, the correct lane is determined for over 90% of the samples; (4) Development of a VISSIM position error module to enable simulation analysis of lane determination and lane queue estimation as a function of positioning error; (5) Development of a lane-level intersection queue prediction algorithm; Simulation evaluation of lane determination accuracy which matched the theoretical analysis; and (6) Simulation evaluation of lane queue prediction accuracy as a function of both CAV penetration rate and positioning accuracy. Conclusions of the simulation analysis in item (6) are the following: First, when the penetration rate is fixed, higher queue length estimation error occurs as the position error increases. However, the disparity across different position error levels diminishes with the decrease of penetration rate. Second, as the penetration rate decreases, the queue length estimation error significantly increases under the same GNSS error level. The current methods that exist for queue length prediction only utilize vehicle position and a penetration rate estimate. These results motivate the need for new methods that more fully utilize the information available on CAVs (e.g., distance to vehicles in front, back, left, and right) to decrease the sensitivity to penetration rate
Entwicklung und Evaluierung eines kooperativen Interaktionskonzepts an Entscheidungspunkten für die teilautomatisierte, manöverbasierte Fahrzeugführung
Moderne Fahrerassistenzsysteme ermöglichen einen hohen Standard hinsichtlich Fahrkomfort und Sicherheit. Eine Lösung für die Problematik zunehmender Komplexität durch Kombination mehrerer Einzelsysteme und einen wichtigen Schritt in Richtung Vollautomatisierung bieten teilautomatisierte, kooperative Ansätze wie das manöverbasierte Fahrzeugführungskonzept Conduct-by-Wire.
Gegenstand dieser Arbeit ist die Untersuchung der Fragestellung, ob eine kooperative Interaktion zwischen Fahrer und Automation zur Entscheidungsfindung hinsichtlich der Ausführbarkeit von Fahrmanövern im Kontext der teilautomatisierten, manöverbasierten Fahrzeugführung darstellbar ist. In dieser Arbeit wird ein Interaktionskonzept entwickelt, das die Anforderungen des Fahrers und der Automation gleichermaßen berücksichtigt. Zudem erfolgt eine Untersuchung der technischen Realisierbarkeit sowie der Gebrauchstauglichkeit im Rahmen einer Probandenstudie
Holistic Temporal Situation Interpretation for Traffic Participant Prediction
For a profound understanding of traffic situations including a prediction of traf-
fic participants’ future motion, behaviors and routes it is crucial to incorporate all
available environmental observations. The presence of sensor noise and depen-
dency uncertainties, the variety of available sensor data, the complexity of large
traffic scenes and the large number of different estimation tasks with diverging
requirements require a general method that gives a robust foundation for the de-
velopment of estimation applications.
In this work, a general description language, called Object-Oriented Factor Graph
Modeling Language (OOFGML), is proposed, that unifies formulation of esti-
mation tasks from the application-oriented problem description via the choice
of variable and probability distribution representation through to the inference
method definition in implementation. The different language properties are dis-
cussed theoretically using abstract examples.
The derivation of explicit application examples is shown for the automated driv-
ing domain. A domain-specific ontology is defined which forms the basis for
four exemplary applications covering the broad spectrum of estimation tasks in
this domain: Basic temporal filtering, ego vehicle localization using advanced
interpretations of perceived objects, road layout perception utilizing inter-object
dependencies and finally highly integrated route, behavior and motion estima-
tion to predict traffic participant’s future actions. All applications are evaluated
as proof of concept and provide an example of how their class of estimation tasks
can be represented using the proposed language. The language serves as a com-
mon basis and opens a new field for further research towards holistic solutions
for automated driving
Lane-Precise Localization with Production Vehicle Sensors and Application to Augmented Reality Navigation
This works describes an approach to lane-precise localization on current digital maps. A particle filter fuses data from production vehicle sensors, such as GPS, radar, and camera. Performance evaluations on more than 200 km of data show that the proposed algorithm can reliably determine the current lane. Furthermore, a possible architecture for an intuitive route guidance system based on Augmented Reality is proposed together with a lane-change recommendation for unclear situations
カメラ画像と汎用センサの統合による自動車位置推定の研究
東京海洋大学博士学位論文 平成29年度(2017) 応用環境システム学 課程博士 甲第479号指導教員名: 久保信明全文公表年月日: 2018-06-20東京海洋大学201