Deep Occupancy-Predictive Representations for Autonomous Driving
Eivind Meyer,Lars Frederik Peiss,Matthias Althoff,Eivind Meyer,Lars Frederik Peiss,Matthias Althoff
Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural motion planners for autonomous vehicles. Instead, this work proposes to learn which features are task-relevant. Given its immediate relevance to motion planning, our proposed architecture encodes the probabilistic occupanc...
