AutoCoach: Driving Behavior Management Using Intelligent IoT Services AutoCoach is an intelligent agent intended for improving automobile driversu0027 performance by applying persuasive technology. System models like Advanced driverassistance (ADAS) and some UsagebasedInsurance (UBI) share an aim to increase car and road safety. However, most prior models do not consider the differences between driving habits. The AutoCoach design includes two unique components to build an effective persuasive system. The first component is the personality classification, which recognizes driversu0027 personalities by analyzing driving behavior patterns. The second component is the rewarding system, which determines the current driving behavioru0027s risk score based on some immediate past behavior. We propose the idea of memory factor, which decides when to provide feedback to drivers based on their personality. This memory factor identifies the most critical behaviors within a flexible timeperiod. AutoCoach then decides on feedback to maintain safe driving or improve the level of awareness for risky driving habits.

Jointly Learnable Behavior and Trajectory Planning for SelfDriving Vehicles The motion planners used in selfdriving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior planner, which handles highlevel decisions and produces a coarse trajectory, and trajectory planner that generates a smooth, feasible trajectory for the duration of the planning horizon. These planners, however, are typically developed separately, and changes in the behavior planner might affect the trajectory planner in unexpected ways. Furthermore, the final trajectory outputted by the trajectory planner might differ significantly from the one generated by the behavior planner, as they do not share the same objective. In this paper, we propose a jointly learnable behavior and trajectory planner. Unlike most existing learnable motion planners that address either only behavior planning, or use an uninterpretable neural network to represent the entire logic from sensors to driving commands, our approach features an interpretable cost function on top of perception, prediction and vehicle dynamics, and a joint learning algorithm that learns a shared cost function employed by our behavior and trajectory components. Experiments on realworld selfdriving data demonstrate that jointly learned planner performs significantly better in terms of both similarity to human driving and other safety metrics, compared to baselines that do not adopt joint behavior and trajectory learning.