tm Autonomous driving made safe
Founder, Bio Celite Milbrandt Austin, Texas since 1998 Founder of Slacker Radio In dash for Tesla, GM, and Ford. 35M active users 2008 Chief Product Officer of RideScout Acquired by MBUSA/Daimler 2014
Mission statement: Making autonomous vehicle travel safe
Current scenario verification challenges Large vehicle fleets Driver to manage in the event of system error Expensive/ad hoc and incomplete. Many simple scenarios are missed Scenario generation and verification happens in real time, and is not easily repeatable
Solution Automate scenario test generation for planning testing Deep learning system for automated scenario modification and re-generation. Leverages existing gaming systems to enable multiphysics simulation Generation of realistic Lidar, Radar, Camera, and IMU sensor information for perceptions system testing Enable automated vehicle control performance metrics Fast error case regeneration, with derivative regeneration
Testing Perception and Planning Ground Truth w/ Scene Labeling Simulation Engine Lidar Camera Stereo Vision Control System Under Test Radar Inertial Measurement Unit (IMU)
Training Realistic Traffic Behavior Each agent/driver must have separate behaviors Behaviors must be learned based on different reward structures during training Examples of learned behaviors Speeder Brake Happy Cell Phone Driver Drunk Driver Behaviors are distributed based on the type of scenarios we want to test against Accidents result based on distribution of agents with various learned behaviors
Reinforcement Trained Neural Network rewards Neuron Updater Input layer is an image in our case Output layers are log probability to apply throttle or turn right. More negative log probabilities represent apply brake or turn left, respectively Number of layers and number of neurons for each layer are selected based on the convergence characteristic given your desired value function and or policy. Reward function is chosen based on desired behavior you are trying to emulate Comment: control belongs in CPU, computation lives in GPU
Reinforcement Learning Simulator Interface Socket-based Python, C++ Single simulator instance Per Agent Reward Modifiers Library of reward modifiers Agent Hyperparameters Continuous action space Multiple concurrent agents Downsampling Full resolution -> 80x80 Top down view or perspective Downsampler N Reward Modifier nxm P(throttle s) Agent P(turnRight s)
Learning to Drive Example of basic reward system Stay in lane Don t hit other vehicles Maintain safe distance from leading vehicle Change lanes only to avoid collision Basic System Details: Examples of our reward functions for different types of drivers Modulate reward with speed Generate negative/positive rewards based on different collision boundaries Generate reward for cause opposing cars to move, swerve, or change direction
Scalable multi-agent training and testing for A3C...
Example monodrive Reinforcement Agent Andrej Karpathy # agent based on karpathy http://karpathy.github.io/2016/05/31/rl/ Up to 20 agents (200 future) Continuous action space Reward based on agent reward function/modifier
www.monodrive.io Try it out! Download simulator at www.monodrive.io Coming soon! Early version available with request to info@monodrive.io Download sample agent and sample reward at: www.github.com/celite/agent_cm.py System Requirements: Windows, Mac, Ubuntu Tensorflow-GPU Or Tensorflow if you have more time than money 32 Gb memory (64GB recommended) Example Agent is python based but can be anything. Control Interface based on IP sockets Downsampler N Reward Modifier nxm P(throttle s) Agent P(turnRight s)
Contact Information info@monodrive.io tm