Environment perception for inner-city driver assistance and highly-automated driving

Gläser, Claudius; Michalke, Thomas; Bürkle, Lutz; Niewels, Frank

doi:10.1109/ivs.2014.6856388

Cited by 16 publications

(8 citation statements)

References 12 publications

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“…Due to the complexity of Level 2 partial automation systems, the sensor configurations are usually more complex. In contrast to assisted driving systems, where one sensor system or technology is directly integrated with the functionality, partial automation systems are the first to introduce the concept of an environment model in the vehicle's architecture [125,128,228]. The environment model combines the data from many sensors and provides the ADAS with an abstracted and generic description of the vehicle's environment.…”

Section: Severalmentioning

confidence: 99%

Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

Aeberhard¹

2017

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Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. The work presented in this thesis has been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic. Contents Abbreviations VIII List of Symbols X Abs...

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Section: Severalmentioning

confidence: 99%

Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

Aeberhard¹

2017

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Section: Introductionmentioning

confidence: 99%

“…In such scenarios, the best alternative is to apply the vehicle brakes with the safe maximum force to minimise the consequences of the unavoidable impact [ 19 ]. EBA primarily consists of two parts [ 20 ]: Detection: identify critical scenarios which can lead to an accident and warn the driver accordingly through audio and/or visual indications; Action: in scenarios where impacts or accidents are inevitable, EBA can decrease the speed of the ego-vehicle by applying brakes in advance to achieve minimal impact. …”

Section: Introductionmentioning

confidence: 99%

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deo¹,

Palade

Huda

2021

Sensors

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Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sensors. As mono-sensor systems cannot provide reliable and consistent readings under all circumstances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substantially improving the reliability of the multi-sensor-based automotive systems. This paper first highlights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentralised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20fps or higher) on an Intel i5-based Ubuntu system, it was concluded through the experiments and analytical comparisons that the decentralised fusion-driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion-driven EBA yields comparatively less accurate results, but with the benefits of a higher frame rate and lesser computational cost.

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“…In earlier works, a binary Bayes filter has been usually employed to combine the measurements of subsequent time steps to a cell estimate, where the cell state is defined as occupied or free under the assumption of a static environment [1]. Occupancy grid maps have been also made popular in autonomous driving for environment representation, free space detection or collision avoidance [10]. However, the static environment assumption does not hold due to moving road users, such as pedestrians, vehicles and cyclists.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks

Schreiber

Hoermann

Dietmayer

2019

2019 International Conference on Robotics and Automation (ICRA)

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In this work, we tackle the problem of modeling the vehicle environment as dynamic occupancy grid map in complex urban scenarios using recurrent neural networks. Dynamic occupancy grid maps represent the scene in a bird's eye view, where each grid cell contains the occupancy probability and the two dimensional velocity. As input data, our approach relies on measurement grid maps, which contain occupancy probabilities, generated with lidar measurements. Given this configuration, we propose a recurrent neural network architecture to predict a dynamic occupancy grid map, i.e. filtered occupancy and velocity of each cell, by using a sequence of measurement grid maps. Our network architecture contains convolutional long-short term memories in order to sequentially process the input, makes use of spatial context, and captures motion. In the evaluation, we quantify improvements in estimating the velocity of braking and turning vehicles compared to the state-of-the-art. Additionally, we demonstrate that our approach provides more consistent velocity estimates for dynamic objects, as well as, less erroneous velocity estimates in static area.

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Environment perception for inner-city driver assistance and highly-automated driving

Cited by 16 publications

References 12 publications

Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks

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