Machine learning (ML)-enabled approaches are considered a substantial support technique of detection and classification of obstacles of traffic participants in self-driving vehicles. Major breakthroughs have been demonstrated the past few years, even covering complete end-to-end data processing chain from sensory inputs through perception and planning to vehicle control of acceleration, breaking and steering. YOLO (you-only-look-once) is a state-of-the-art perception neural network (NN) architecture providing object detection and classification through bounding box estimations on camera images. As the NN is trained on well annotated images, in this paper we study the variations of confidence levels from the NN when tested on hand-crafted occlusion added to a test set. We compare regular pedestrian detection to upper and lower body detection. Our findings show that the two NN using only partial information perform similarly well like the NN for the full body when the full body NN’s performance is 0.75 or better. Furthermore and as expected, the network, which is only trained on the lower half body is least prone to disturbances from occlusions of the upper half and vice versa.

The software testing state of practice is not as good as it ought to be. To reduce the gap between the state of practice and the state of the art, improvement in software testing is necessary. Existing improvement models focus too little on, or lack important aspects of, software testing. Therefore a new model is required. This paper describes a test improvement model called TIM. The model functions as a guidebook for test improvement. It is mainly inspired by SEI's Capability Maturity Model and Gelperin's Testability Maturity Model. TIM introduces new perspectives to test improvement, with explicit focus on cost-effectiveness and risk management. The model addresses five ‘key areas’ of testing: organization, planning and tracking, test cases, testware and reviews. TIM also includes an assessment procedure. The model is continuously revised as knowledge is gained through research and experience of use. This paper presents results from three applications of TIM. © 1997 John Wiley & Sons, Ltd.