In autonomous cars accurate and reliable detection of objects in the proximity of the vehicle is necessary in order to perform further safety-critical actions which depend upon it. Many detectors have been developed in the last few years, but there is still demand for more reliable and more robust detectors. Some detectors rely on a single sensor, while some others are based upon fusion of data from multiple sources. The main aim of this paper is to show how image features can contribute to performance improvement of detectors which rely on point cloud data only. In addition it will be shown, how lidar reflectance data can be substituted by low-level image features without degrading the performance of detectors. Three different approaches are proposed to fuse image features with point cloud data. The extended networks are compared with the original network and tested on a well-known dataset and on our own data, as well. This might be important when the same pre-trained model is to be used on data generated by a lidar using different reflectance encoding schemes and when due to the lack of training data retraining is not possible. Different augmentation techniques have been proposed and tested on the KITTI dataset as well as on data acquired by a different lidar sensor. The networks augmented with image features achieved a recall increase of a few percent for occluded objects.
Vehicle dynamics models are widely used in many areas of the automotive industry. The usability of each model depends on how well it is able to mimic the behavior of the real vehicle. Each simulation model must go through a thorough investigation process first, which is called model validation. Although, vehicle dynamics simulation models and methodology for computational model validation are well established fields, to the best of the authors' knowledge a general framework for vehicle dynamics model validation is still lacking. The research aims to develop a comprehensive methodological framework for vehicle dynamics model validation. In this paper the aim is to present the high level layout of the proposed framework, introducing the main blocks and the tasks related, also addressing some critical issues regarding vehicle dynamics model validation such as validation metrics and vehicle parameter measurement and estimation. An important part of the proposed methodology is a sophisticated vehicle dynamics measurement system, which gives the opportunity to estimate a bunch of vehicle parameters during dynamic testing, which can be useful for several reasons, e.g. fine-tuning the parameters of the Pacejka Magic formula. As a case study some vehicle dynamics test based parameter estimations are shown to justify the raison d'être and investigate possible applications. INDEX TERMS Vehicle model validation, vehicle dynamics, vehicle parameter identification, methodological framework.
Mánuel Gressai, Balázs Varga, Tamás Tettamanti, István Varga
Communications in Transportation Research
Road traffic congestion has become an everyday phenomenon in today's cities all around the world. The reason is clear: at peak hours, the road network operates at full capacity. In this way, growing traffic demand cannot be satisfied, not even with traffic-responsive signal plans. The external impacts of traffic congestion come with a serious socio-economic cost: air pollution, increased travel times and fuel consumption, stress, as well as higher risk of accidents. To tackle these problems, a number of European cities have implemented reduced speed limit measures. Similarly, a general urban speed limit measure is in preparatory phase in Budapest, Hungary. In this context, a complex preliminary impact assessment is needed using a simulated environment. Two typical network parts of Budapest were analyzed with microscopic traffic simulations. The results revealed that speed limits can affect traffic differently in diverse network types indicating that thorough examination and preparation works are needed prior to the introduction of speed limit reduction.