The developed agent sensorimotor system (Objective 1) was guided by a number of theoretical ideas in robotics and cognition. The agent is realised by an algorithmic scaffolding (figure 1), producing large-scale functions of different types, hosting learning modules. There are 5 main loops: 1) parallel action priming, 2) action-selection generating emergent adaptive behaviours, 3) a logical module which implements biases in action-selection steering the behaviour according to traffic rules, 4) a loop learning predictive models, 5) a loop which implements inverse model control.
Learning of behaviour and control (Objective 2) follows a hierarchy of motor abilities (figure 2). At ‘wake state’, predictive models of the vehicle dynamics are learned (declarative prediction models may also be learned within loop 1). Offline, at ‘dream state’, inverse models are trained to synthetize the inverse dynamics in a carefully crafted set of situations (episodes). The process proceeds by levels of increasing competence: predictive control, short-term goal-directed actions, etc.
A large number of experiments have been carried out while progressively training the motor abilities of the agent (Objective 3). The agent was trained to drive three different types of vehicles demonstrating interoperability. In addition, a fourth driving simulation environment, open for research and evaluation was released with examples of simulation scenarios, data and training procedures (D5.5).
A number of standardized tests have been created by adapting the Euro NCAP test scenarios to the autonomous driving case (Objective 4). These include basic functionalities like lane keeping, speed adaptation, obstacle avoidance as well as more complex situations like complex traffic in motorways and in urban scenarios. The successful progress of the agent abilities has been monitored across the development phase.
A large number of organised activities have been carried out for dissemination and exploitations. A rough count indicates 36 participations to workshops, conferences, talks, 22 scientific papers (that will continue to increase after project end), 10 communications actions, 6 organised workshops, 18 different liaisons activities, 7 exploitation-oriented liaisons and 1 post laureate Master course in Autonomous Driving Technologies.
Dreams4Cars today holds a portfolio of methods and know-how that is suited for exploitation. The exploitation strategy is to provide support methods for development of self-driving and driver-assistance functions. With this respect, the methods of Dreams4Cars do not need to be adopted altogether. Instead, they can also be adopted progressively (for example beginning with predictive/robust/adaptive control with learned dynamics), hence integrating in industrial workflows without disrupting impact.
Scientific results (in particular the “learning via embodied/episodic simulation”) and the open simulation environment are other non-commercial exploitation opportunities for (independent) studies, e.g. related to human-agent interactions in the driving simulator.
