Distant Vehicle Detection Using Radar and Vision

For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using convolutional neural networks exhibit excellent performance on existing datasets such as KITTI. However, the performance of these networks falls when detecting small (distant) objects. We demonstrate that incorporating radar data can boost performance in these difficult situations. We also introduce an efficient automated method for training data generation using cameras of different focal lengths

Adaptive Multi-sensor Perception for Driving Automation in Outdoor Contexts

In this research, adaptive perception for driving automation is discussed so as to enable a vehicle to automatically detect driveable areas and obstacles in the scene. It is especially designed for outdoor contexts where conventional perception systems that rely on a priori knowledge of the terrain's geometric properties, appearance properties, or both, is prone to fail, due to the variability in the terrain properties and environmental conditions. In contrast, the proposed framework uses a self-learning approach to build a model of the ground class that is continuously adjusted online to reflect the latest ground appearance. The system also features high flexibility, as it can work using a single sensor modality or a multi-sensor combination. In the context of this research, different embodiments have been demonstrated using range data coming from either a radar or a stereo camera, and adopting self-supervised strategies where monocular vision is automatically trained by radar or stereo vision. A comprehensive set of experimental results, obtained with different ground vehicles operating in the field, are presented to validate and assess the performance of the system

Review of Environment Perception for Intelligent Vehicles

Overview of environment perception for intelligent vehicles supposes to the state-of-the-art algorithms and modeling methods are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. Integrated lane and vehicle tracking for driver assistance system that improves on the performance of both lane tracking and vehicle tracking modules. Without specific hardware and software optimizations, the fully implemented system runs at near-real-time speeds of 11 frames per second. On-road vision-based vehicle detection, tracking, and behavior understanding. Vision based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor–vision fusion for on-road vehicle detection. The traffic sign detection detailing detection systems for traffic sign recognition (TSR) for driver assistance. Inherently in traffic sign detection to the various stages: segmentation, feature extraction, and final sign detection