End-to-End Autonomous Driving
End-to-end autonomous driving is a system architecture paradigm that consolidates perception, prediction, planning, and control modules into a single unified learnable neural network. Compared with traditional modular pipeline approaches, it enables stronger end-to-end optimization across the full driving stack.
Timeline (last 90 days)
End-to-end autonomous driving solutions attempt to use a single unified neural network to directly map raw inputs from sensors such as cameras and LiDAR to control commands such as steering angle, throttle, and braking, eliminating manually designed intermediate modules.
End-to-end autonomous driving solutions train a unified large neural network that takes raw perception data from cameras, LiDAR, and other sensors as input and directly outputs steering angles and throttle/brake commands.
All Facts (2)
End-to-end autonomous driving solutions attempt to use a single unified neural network to directly map raw inputs from sensors such as cameras and LiDAR to control commands such as steering angle, throttle, and braking, eliminating manually designed intermediate modules.
50%UnverifiedEnd-to-end autonomous driving solutions train a unified large neural network that takes raw perception data from cameras, LiDAR, and other sensors as input and directly outputs steering angles and throttle/brake commands.
50%