OpenCalib: A multi-sensor calibration toolbox for autonomous driving

Multi-Modal Multi-Task Deep Learning for Autonomous Driving

Several deep learning approaches have been applied to the autonomous driving task, many employing end-toend deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not factor in the different behavioral modalities of the driving task into the training strategy. This pap...

Autonomous Sensor Center Position Calibration with Linear Laser-Vision Sensor

A linear laser-vision sensor called 'Perception TriCam Contour' is mounted on an industrial robot and often used for various application of the robot such as the position correction and the inspection of a part. In this paper, a sensor center position calibration is presented for the most accurate use of the robot-Perceptron system. The obtained algorithm is suitable for on-site calibration in ...

Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must ba...

Modeling and Simulation of Sensor-Guided Autonomous Driving

Abstract: This paper presents a modeling and simulation method for sensorguided autonomous driving. A generic model of range sensing and object detection in 3D space is discussed first that represents their high-level functions. The low-level physical characteristics of range sensing and object detection are further investigated based on frequency modulated continuous wave (FMCW) radar which is...

Electromembrane Processes, a Toolbox for Advanced Separations