12 research outputs found
Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data
Accurate 7DoF prediction of vehicles at an intersection is an important task
for assessing potential conflicts between road users. In principle, this could
be achieved by a single camera system that is capable of detecting the pose of
each vehicle but this would require a large, accurately labelled dataset from
which to train the detector. Although large vehicle pose datasets exist
(ostensibly developed for autonomous vehicles), we find training on these
datasets inadequate. These datasets contain images from a ground level
viewpoint, whereas an ideal view for intersection observation would be elevated
higher above the road surface. We develop an alternative approach using a
weakly supervised method of fine tuning 3D object detectors for traffic
observation cameras; showing in the process that large existing autonomous
vehicle datasets can be leveraged for pre-training. To fine-tune the monocular
3D object detector, our method utilises multiple 2D detections from
overlapping, wide-baseline views and a loss that encodes the subjacent
geometric consistency. Our method achieves vehicle 7DoF pose prediction
accuracy on our dataset comparable to the top performing monocular 3D object
detectors on autonomous vehicle datasets. We present our training methodology,
multi-view reprojection loss, and dataset.Comment: Paper accepted at The 32nd British Machine Vision Conference, BMVC
202
Data-Importance-Aware Bandwidth-Allocation Scheme for Point-Cloud Transmission in Multiple LIDAR Sensors
This paper addresses bandwidth allocation to multiple light detection and ranging (LIDAR) sensors for smart monitoring, which a limited communication capacity is available to transmit a large volume of point-cloud data from the sensors to an edge server in real time. To deal with the limited capacity of the communication channel, we propose a bandwidth-allocation scheme that assigns multiple point-cloud compression formats to each LIDAR sensor in accordance with the spatial importance of the point-cloud data transmitted by the sensor. Spatial importance is determined by estimating how objects, such as cars, trucks, bikes, and pedestrians, are likely to exist since regions where objects are more likely to exist are more useful for smart monitoring. A numerical study using a real point-cloud dataset obtained at an intersection indicates that the proposed scheme is superior to the benchmarks in terms of the distributions of data volumes among LIDAR sensors and quality of point-cloud data received by the edge server
Learning Motion Primitives Automata for Autonomous Driving Applications
Motion planning methods often rely on libraries of primitives. The selection of primitives
is then crucial for assuring feasible solutions and good performance within the motion planner.
In the literature, the library is usually designed by either learning from demonstration, relying
entirely on data, or by model-based approaches, with the advantage of exploiting the dynamical
systemâs property, e.g., symmetries. In this work, we propose a method combining data with a
dynamical model to optimally select primitives. The library is designed based on primitives with
highest occurrences within the data set, while Lie group symmetries from a model are analysed
in the available data to allow for structure-exploiting primitives. We illustrate our technique in
an autonomous driving application. Primitives are identified based on data from human driving,
with the freedom to build libraries of different sizes as a parameter of choice. We also compare
the extracted library with a custom selection of primitives regarding the performance of obtained
solutions for a street layout based on a real-world scenario
Collective Perception: A Safety Perspective
Vehicle-to-everything (V2X) communication is seen as one of the main enabling technol-ogies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to âsee through the eyes of othersâ by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redun-dancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic par-ticipants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only con-tributes to the accuracy and integrity of the vehiclesâ environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a ârisk of serious traffic accidentsâ to a âresidual hypothetical risk of collisions without minor injuriesâ for traffic participants equipped with non-redundant 360° sensor systems. These results support the ongoing world-wide standardization efforts of the collective perception service
Naturalistic Driver Intention and Path Prediction using Machine Learning
Autonomous vehicles are still yet to be available to the public. This is because there are a number of challenges that have not been overcome to ensure that autonomous vehicles can safely and efficiently drive on public roads. Accurate prediction of other vehicles is vital for safe driving, as interacting with other vehicles is unavoidable on public streets. This thesis explores reasons why this problem of scene understanding is still unsolved, and presents methods for driver intention and path prediction. The thesis focuses on intersections, as this is a very complex scenario in which to predict the actions of human drivers. There is very limited data available for intersection studies from the perspective of an autonomous vehicle. This thesis presents a very large dataset of over 23,000 vehicle trajectories, used to validate the algorithms presented in this thesis. This dataset was collected using a lidar based vehicle detection and tracking system onboard a vehicle. Analytics of this data is presented. To determine the intent of vehicle at an intersection, a method for manoeuvre classification through the use of recurrent neural networks is presented. This allows accurate predictions of which destination a vehicle will take at an unsignalised intersection, based on that vehicle's approach. The final contribution of this thesis presents a method for driver path prediction, based on recurrent neural networks. It produces a multi-modal prediction for the vehicleâs path with uncertainty assigned to each mode. The output modes are not hand labelled, but instead learned from the data. This results in there not being a fixed number of output modes. Whilst the application of this method is vehicle prediction, this method shows significant promise to be used in other areas of robotics