Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

Effectiveness evaluation of the interventions. Deliverable 7.2 of the EC H2020 project i-DREAMS

The overall objective of the i-DREAMS project is to set up a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment was made to monitor and determine if a driver is within acceptable boundaries of safe operation (i.e., Safety Tolerance Zone). Moreover, the i-DREAMS platform offers a series of in-vehicle interventions, meant to prevent drivers from getting too close to the boundaries of unsafe operation and to bring them back into the safety tolerance zone while driving. This deliverable focusses on evaluating the effectiveness of the i-DREAMS interventions in improving drivers’ safety outcomes. The work here will evaluate the impact of the real-time driver interventions, post-trip driver feedback, and gamification interventions, in order to assess their impact on driving behaviour and driver state. Comparisons will be made between the different countries for which data are available, between the different interventions, and between the different outcome variables. The data collected in on-road field trials are analysed for private drivers (passenger cars) and professional drivers (trucks and busses). The analysis of the interventions is formed of two main areas: outcome evaluation and process evaluation. Outcome evaluation, also known as effect evaluation, measures the effectiveness of the intervention. More specifically, it assesses whether the targeted factors of the on-road trials changed as a result of the intervention or not. The outcome evaluation of the on-road trials will examine whether the i-DREAMS interventions influenced the following four areas: safety outcomes, safety promoting goals, performance objectives, and change objectives. These four areas are part of the logic model of change behind the i-DREAMS interventions. Process evaluation assesses which parts of the intervention were implemented as intended, and which were not. For private drivers, the results show that there was a statistically significant decrease in events from Phase 1 to Phase 4. This suggests that the i-DREAMS system had a positive impact on the measured safety outcomes and succeeded in keeping drivers in the first level of the STZ for more of their journey. When individual phase changes are considered, the most significant results were seen from Phase 3 to Phase 4. This suggest that the addition of the gamification elements had a significant impact on safety outcomes, and further supports the conclusion that the full system provides the most effective results. However, differences were found when each country was analysed individually, which were statistically significant, though there is not a clear reason why this would be so. Furthermore, differences were also found between drivers within countries. In each country, between two thirds to three quarters of drivers showed improved outcomes (i.e., a reduction in events), but the remainder had worse outcomes (an increase in events). It’s not clear from the data why some individuals responded positively to the technology and others did not, and further work is needed to understand why the system has such varied effects on different drivers. For all countries, drivers engaged more with the app in Phase 4 of the trial compared with Phase 3, after the introduction of the gamification features. Although the ‘trips’ and ‘scores’ menu were the functions most used by drivers (functions that were available in both phases), the data suggests that the gamification functions were more engaging and held attention more consistently. The generic information in the app (hints, tips etc.) was less appealing to users. They found more interest in personalised feedback such as their trip information, goals, and position on the leader board. The data also suggests a link between app usage and performance outcome; nearly all the drivers who used the app heavily showed improved outcomes. It would be interesting to investigate this further to determine whether there is a causal effect between these results. Generally, the i-DREAMS system showed less positive impact with professional drivers compared to private drivers. Specifically, a lower proportion of the professional drivers showed improved outcomes, and little significant change was seen in terms of safety outcomes. Where there were significant results, these were most often increases in events, i.e., worse outcome. Again, it is not obvious why this result is observed. The only statistically significant improved outcome was for truck drivers, which was for ‘total’ high severity events specifically between Phase 1 and Phase 2. Therefore, it can tentatively be concluded that the system had a positive impact on the most severe events. Process evaluation results were only available for Truck drivers, but showed similar results to private drivers, with more app engagement in Phase 4 compared to Phase 3, after the introduction of gamification features. This further supports the value of gamification features. The intention was to use the results to inform the ranking of interventions and provide an assessment of which intervention schemes are most effective. However, given the varied results between countries and transport modes, it is difficult to conclude a definitive ranking of the different interventions. The results indicate that the full system (real-time warnings plus app feedback plus gamification features in the app) provides the most significant positive impact on driver outcome. For private drivers, the analysis showed that most significant positive change was seen in Phase 4 of the trial, i.e., the gamification features, however it cannot be said that those alone were the most effective, as they were tested in combination with the other interventions. However, the data does suggest that app feedback on its own is less effective than when the app also includes gamification features. For truck drivers, we can tentatively conclude that the real-time interventions had the most impact, however more data is needed to support this. Lastly, the rail mode was included in i-DREAMS to broaden the application of the i-DREAMS platform which was originally designed for use in road vehicles. Trams operate within a mixed-traffic environment, driving on both segregated track, and shared, multi-user road. Therefore, aspects of the i-DREAMS platform can be applied to trams and may be beneficial to tram driving safety and risk mitigation. Two main studies were carried out to assess the use of the i-DREAMS platform in trams. The first was a simulator study to test the real-time element of the platform and the second was a focus group study to assess the potential use of the post-trip feedback app in the tram context. The tram simulator study suggests that the i-DREAMS system and associated warnings offer several benefits for tram driving operations. Firstly, as instances of speeding are rare, the speed alert would be more helpful as a warning before the occurrence of speeding, alerting the drivers they are approaching the limit, or more effective as a constant in-cab reminder of the current speed limit. The concept of a vulnerable road user (VRU) warning could be beneficial to tram drivers operating in mixed traffic environments, however, it was clear that the VRU warning needs to be developed to take into account specific aspects of tram driving and there is a concern about it being triggered too often. The fatigue warning could also potentially be beneficial as a warning before the existing fatigue monitoring device alerts, as a prompt to drivers to consider their alertness or take a break. Tram drivers suggested that the app would be most useful in identifying issues that were common to drivers and as a self-evaluation tool. They were more sceptical about the gamification elements, in particular the leader board, and expressed views that competition could have a negative impact on safety and is therefore not desired.</p