Hans-Joachim Wuensche

Bio: Hans-Joachim Wuensche is an academic researcher from Bundeswehr University Munich. The author has contributed to research in topics: Stereo camera & Video tracking. The author has an hindex of 13, co-authored 90 publications receiving 585 citations.

Motion planning for autonomous vehicles in highly constrained urban environments

Abstract: In this paper, we present a motion planning algorithm for autonomous navigation in highly constrained urban environments. Since common approaches to on-road trajectory planning turned out to be unsuitable for this task, we instead extended an A*-based planner originally designed for navigation in unstructured environments. Two novel node expansion methods were added to obtain smooth and accurate trajectories that consider the structure of the environment. The first one attempts to find a trajectory connecting the current node directly to the goal by solving a boundary value problem using numerical optimization. The second method leverages a simulated pure-pursuit controller to generate edges (i.e. short motion primitives) that guide the vehicle toward or along the global reference path. As a result, the planner is able to produce smooth paths while retaining the explorative power of A* that is needed to deal with challenging situations in urban driving (e.g., reversing in order to pass a vehicle that stopped unexpectedly). Its practical usefulness was demonstrated during extensive tests on an electric vehicle navigating a mock urban environment as well as on our own autonomous vehicle MuCAR-3.

Radar reflection characteristics of vehicles for contour and feature estimation

Abstract: Accurate environmental perception is a key requirement for autonomous driving. While the robust and precise estimation of the dynamic state of nearby objects is sufficient for ordinary driver assistance systems like adaptive cruise control, higher levels of autonomy require knowledge of the extent of objects for measurement data association and path finding algorithms. Extent estimation is known to be robustly accomplished by lidar sensors as they provide low measurement noise and a high resolution. Radar-based extent estimation, however, would be more cost-efficient if sufficient robustness would be given. In this work, we will examine the radar characteristics of vehicles using commercial off-the-shelf radars providing cluttered detection data, and evaluate its potential for extent estimation. This is done by observing measurement data from test vehicles with a known position from a spectrum of views, i.e. combinations of distances and orientations. We perform an appropriate measurement analysis and compare the obtainable extent estimate with the true contour. Besides, we consider radar-only vehicle features. The aim of this work is the acquisition of knowledge about typical radar reflection characteristics of vehicles employing a large data basis which can be subsequently utilized in online extent estimation algorithms.

Heterogeneous multi-sensor fusion for extended objects in automotive scenarios using Gaussian processes and a GMPHD-filter

Abstract: Modern advanced driver assistance systems (ADAS) and automated driving functions for automobiles rely on an accurate model of the environment. To this end, the exploitation of complementary advantages of the measurement principles used by radar, lidar and camera sensors is an important prerequisite. We develop a framework for sensor data fusion that incorporates heterogeneous sensor data from multiple sensors in a modular way. In order to use as much genuine information as possible, the measurement data is utilized in its raw low level form or abstracted to a single frame feature representation. Historically, an approach with local decentralized tracks obtained from the individual sensors prevails. This method does not offer true multiple hypothesis considerations in a straight-forward manner. Additionally, a mathematically correct sensor data fusion in the high level approach is infeasible when the covariances of the local tracks are not transmitted. In contrast to this, the full and uncorrelated information contained in the individual measurements provides the possibility for a correct fusion of data and enables a probabilistic conflict resolution of the data association problem. With regard to the multiple hypothesis problem, the Gaussian mixture probability hypothesis density (GMPHD) filter provides a solution. For the estimation of the extent of the observed objects, Gaussian processes offer the possibility to model shapes with a considerable amount of flexibility by using functions which represent the contour of the objects. To demonstrate first results of our approach, we show results with real experimental data from one laser scanner and four short range radars.

Trajectory planning for car-like robots in unknown, unstructured environments

Abstract: We describe a variable-velocity trajectory plan- ning algorithm for navigating car-like robots through unknown, unstructured environments along a series of possibly corrupted GPS waypoints. The trajectories are guaranteed to be kine- matically feasible, i.e., they respect the robot's acceleration and deceleration capabilities as well as its maximum steering angle and steering rate. Their costs are computed using LiDAR and camera data and depend on factors such as proximity to obstacles, curvature, changes of curvature, and slope. In a second step, velocities for the least-cost trajectory are adjusted based on the dynamics of the vehicle. When the robot is faced with an obstacle on its trajectory, the planner is restarted to compute an alternative trajectory. Our algorithm is robust against GPS error and waypoints placed in obstacle-filled areas. It was successfully used at euRathlon 2013 1 , where our autonomous vehicle MuCAR-3 took first place in the "Autonomous Navigation" scenario.

GIS-based topological robot localization through LIDAR crossroad detection

Abstract: While navigating in areas with weak or erroneous GPS signals such as forests or urban canyons, correct map localization is impeded by means of contradicting position hypotheses. Thus, instead of just utilizing GPS positions improved by the robot's ego-motion, this paper's approach tries to incorporate crossroad measurements given by the robots perception system and topological informations associated to crossroads within a pre-defined road network into the localization process. We thus propose a new algorithm for crossroad detection in LIDAR data, that examines the free space between obstacles in an occupancy grid in combination with a Kalman filter for data association and tracking. Hence rather than correcting a robot's position by just incorporating the robot's ego-motion in the absence of GPS signals, our method aims at data association and correspondence finding by means of detected real world structures and their counterparts in predefined, maybe even handcrafted, digital maps.

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

Abstract: Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

Digital control of dynamic systems

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