Abstract
Abstract. This work presents an approach to classify road users as pedestrians, cyclists or cars using a lidar sensor and a radar sensor. The lidar is used to detect moving road users in the surroundings of the car. A 2-dimensional range-Doppler window, a so called region of interest, of the radar power spectrum centered at the object's position is cut out and fed into a convolutional neural network to be classified. With this approach it is possible to classify multiple moving objects within a single radar measurement frame. The convolutional neural network is trained using data gathered with a test vehicle in real urban scenarios. An overall classification accuracy as high as 0.91 is achieved with this approach. The accuracy can be improved to 0.94 after applying a discrete Bayes filter on top of the classifier.
Highlights
On the road to highly and fully automated driving, modern cars need to capture their environment, and be able to understand it
The rectified linear unit (ReLU) is defined as f (x) = max(0, x) and it introduces a nonlinear behavior to the network
The same happens in other tracks, where e.g. pedestrians are crossing the street in groups, or when a pedestrian is walking slowly near parked cars. This highlights an inherent weakness of the approach, namely that of overlapping targets, since the train set contains mainly frames where only one single target is present in the region of interest (ROI)
Summary
On the road to highly and fully automated driving, modern cars need to capture their environment, and be able to understand it. While the overall number of fatalities from the vulnerable road users group in the European Union decreased between 2006 and 2015, they still made up in 2015 for 21 % (5435 pedestrians) and 7,8 % (2043 cyclists) of all road accident fatalities (European Commission, 2017a, b) This makes capturing and understanding the cars’ surroundings in urban scenarios of utmost importance. R. Pérez et al.: A machine learning joint lidar and radar classification system in urban automotive scenarios to single target scenarios and not entirely suitable for urban automotive scenarios. Pérez et al.: A machine learning joint lidar and radar classification system in urban automotive scenarios to single target scenarios and not entirely suitable for urban automotive scenarios Another promising approach with origin in the image classification and detection domain is that of semantic segmentation, where each pixel in an image is assigned a class probability vector.
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