Autonomous driving is the task of driving a vehicle without human conduction.
( Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving )
This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicle.
In this paper, we review the latest finding in 3D LIDAR localization for autonomous driving cars, and analyze the results obtained by each method, in an effort to guide the research community towards the path that seems to be the most promising.
The development process in use consists of multiple iterations of data collection, labelling, training, and evaluation.
Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction.
Trajectory forecasting, or trajectory prediction, of multiple interacting agents in dynamic scenes, is an important problem for many applications, such as robotic systems and autonomous driving.
In our experiments the low resolution 16/32 layer LIDAR point clouds are simulated by subsampling the original 64 layer data, for subsequent transformation in to a feature map in the Bird-Eye-View (BEV) and SphericalView (SV) representations of the point cloud.
To reduce the communication cost, lossy data compression can be exploited for inference tasks, but may bring more erroneous inference results.
The kind of closed-loop verification likely to be required for autonomous vehicle (AV) safety testing is beyond the reach of traditional test methodologies and discrete verification.