Autonomous navigation in interaction-based environments - a case of non-signalised roundabouts

To reduce the number of collision fatalities at crossroads intersections many countries have started replacing intersections with non-signalised roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviours according to the scenario. A non-signalised roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction dependent scenarios safely, efficiently and comfortably has been a challenge even for human drivers. Unlike traffic signal controlled roundabouts where the merging order is centrally controlled, driving a non-signalised roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an Adaptive Tactical Behaviour Planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviours for navigating a non-signalised roundabout, combining naturalistic behaviour planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behaviour planning approach and ATBP design are described in the paper

Adaptive tactical behaviour planner for autonomous ground vehicle

Success of autonomous vehicle to effectively replace a human driver depends on its ability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through scenarios such as intersections that demand interactive behavior for successful navigation. The many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state of the art in path planning solutions. They have been found to result in inefficient and sometime unsafe behaviours when tackling interactively demanding scenarios. In this paper we review the current state of the art of path planning solutions, the individual planners and the associated methods for each planner. We then establish a gap in the path planning solutions by reviewing the methods against the objectives for successful path planning. A new adaptive tactical behaviour planner framework is then proposed to fill this gap. The behaviour planning framework is motivated by how expert human drivers plan their behaviours in interactive scenarios. Individual modules of the behaviour planner is then described with the description how it fits in the overall framework. Finally we discuss how this planner is expected to generate safe and efficient behaviors in complex dynamic traffic scenarios by considering a case of an un-signalised roundabout

Human-like motion planning for autonomous ground vehicle applications

To overcome the existing challenges within the passenger car sector such as improving motion safety, reducing traffic congestion and meeting the increasing expectation of drive-comfort, the Intelligent Transportation System community have long envisioned developing autonomous vehicles capable of driving themselves without the need for human intervention. Although the technological progress and drive from the ITS community (academia, OEMs, Govt. etc) has manifested in autonomous vehicle prototypes accumulating millions of autonomously driven miles, critical technological limitations still exist preventing the mass application of the technology on public roads. One important technological limitation of the existing autonomous systems is the inability to successfully negotiate interaction-dependent urban driving scenarios, highlighted by the number of reported collisions/ near misses and autonomous vehicle disengagements during testing. Therefore, most autonomous vehicle testing has been generally restricted to simple road geometries and less dynamic or controlled driving scenarios. There is lack of significant evidence of attempts made to demonstrate autonomous navigation of complex, ambiguous and interaction-dependent scenarios within urban environments, such as non-signalised junctions, shared crossing zones etc. While some human drivers do demonstrate the expert ability of negotiating such scenarios every day, there is a great degree of inconsistency among the human driving populace. This inconsistency with motion behaviour adaptation and decision-making in interaction-dependent scenarios, leads to unsafe, inefficient and uncomfortable driving experience. Successful autonomous vehicle motion planning in such scenarios therefore necessitates inheriting the adaptive behaviour planning with naturalistic manoeuvres and tactical decision-making abilities analogous to “expert” human drivers. This research proposed a novel “human-like” motion planning approach with the characteristics of adaptive motion planning through “naturalistic” trajectory generation and tactical decision-making, The two foremost contribution of this research are the motion planning system framework (HAPS), that enables hybrid forms of decision-making in autonomous vehicle and an integrated local motion planning system (ATBP), which combines the behaviour and trajectory planning system to achieve the desired characteristics of expert human driving. With the proposed approach, the autonomous vehicle was shown to be superior at negotiating interaction-dependent scenarios by outperforming human drivers on the objectives of motion safety (avoid collision and near misses), motion efficiency (reduce navigation time) and motion comfort (maintain accelerations within acceptable limits) in two simulator studies. Furthermore, the application of innovation was demonstrated through the successful implementation and testing of the motion planning system on a real vehicle platform. The autonomous vehicle demonstrated the expert human-like ability to adapt its motion behaviours to firstly negotiate a selected list of highly dynamic driving scenarios in controlled environment and then, drove autonomously in un-controlled free flowing traffic in the first of its kind autonomous demonstrations in two cities in the UK

Adaptive behaviour selection for autonomous vehicle through naturalistic speed planning

As autonomous technologies in ground vehicle application begin to mature, there is a greater acceptance that they can eventually exhaust human involvement in the driving activity. There is however still a long way to go before such maturity is seen in autonomous ground vehicles. One of the critical limitations of the existing technology is the inability to navigate complex dynamic traffic scenarios such as non-signalised roundabouts safely, efficiently and while maintaining passenger drive comfort. The navigation at roundabouts has often been considered as either a problem of collision avoidance alone or the problem of efficient driving (reducing congestion). We argue that for any autonomous planning solution to be accepted for replacing the human driver, it has to consider all the three objectives of safety, efficiency and comfort. With human drivers driving these complex and dynamic scenarios for a long time, learning from the human driving has become a promising area of research. In this work, we learn human driver's longitudinal behaviours for driving at a non-signalised roundabout. This knowledge is then used to generate longitudinal behaviour candidate profiles that give the autonomous vehicle different behaviour choices in a dynamic environment. A decision-making algorithm is then employed to tactically select the optimal behaviour candidate based on the existing scenario dynamics. There are two important contributions in this paper, firstly the adaptive longitudinal behaviour candidate generation algorithm and secondly the tactical, risk aware, multi-objective decision-making algorithm. We describe their implementation and compare the autonomous vehicle performance against human driving