Rethinking Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review. (arXiv:2308.05731v1 [cs.RO])

Automated driving has the potential to revolutionize personal, public, and
freight mobility. Besides the enormous challenge of perception, i.e. accurately
perceiving the environment using available sensor data, automated driving
comprises planning a safe, comfortable, and efficient motion trajectory. To
promote safety and progress, many works rely on modules that predict the future
motion of surrounding traffic. Modular automated driving systems commonly
handle prediction and planning as sequential separate tasks. While this
accounts for the influence of surrounding traffic on the …

arxiv automated challenge data deep learning driving environment freight integration mobility perception planning prediction progress promote public review safety sensor systems trajectory