Ola Benderius

A Review of Near-Collision Driver Behavior Models

Objective: This article provides a review of recent models of driver behavior in on-road collision situations. Background: In efforts to improve traffic safety, computer simulation of accident situations holds promise as a valuable tool, for both academia and industry. However, to ensure the validity of simulations, models are needed that accurately capture near-crash driver behavior, as observed in real traffic or driving experiments. Method: Scientific articles were identified by a systematic approach, including extensive database searches. Criteria for inclusion were defined and applied, including the requirement that models should have been previously applied to simulate on-road collision avoidance behavior. Several selected models were implemented and tested in selected scenarios. Results: The reviewed articles were grouped according to a rough taxonomy based on main emphasis, namely avoidance by braking, avoidance by steering, avoidance by a combination of braking and steering, effects of driver states and characteristics on avoidance, and simulation platforms. Conclusion: A large number of near-collision driver behavior models have been proposed. Validation using human driving data has often been limited, but exceptions exist. The research field appears fragmented, but simulation-based comparison indicates that there may be more similarity between models than what is apparent from the model equations. Further comparison of models is recommended. Application: This review provides traffic safety researchers with an overview of the field of driver models for collision situations. Specifically, researchers aiming to develop simulations of on-road collision accident situations can use this review to find suitable starting points for their work.

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

A number of driver models were fitted to a large data set of human truck driving, from a simulated near-crash, low-friction scenario, yielding two main insights: steering to avoid a collision was best described as an open-loop manoeuvre of predetermined duration, but with situation-adapted amplitude, and subsequent vehicle stabilisation could to a large extent be accounted for by a simple yaw rate nulling control law. These two phenomena, which could be hypothesised to generalise to passenger car driving, were found to determine the ability of four driver models adopted from the literature to fit the human data. Based on the obtained results, it is argued that the concept of internal vehicle models may be less valuable when modelling driver behaviour in non-routine situations such as near-crashes, where behaviour may be better described as direct responses to salient perceptual cues. Some methodological issues in comparing and validating driver models are also discussed.

Evidence for a fundamental property of steering

In this paper, a general and fundamental property of steering is demonstrated: It is shown that steering corrections generally follow bell-shaped profiles of steering rate. The finding is strongly related to what is already known about reaching movements. Also, a strong linear relationship was found between the maximum steering wheel rate and the steering wheel deflection, something that indicates a constant movement time for the correction. Furthermore, by closer examination of those corrections that cannot be described by a single bell-shaped rate profile, it was found that they typically can be described using two or, in some cases three or four, overlapping profiles, something which relates to superposition of motor primitives.

Effects of experience and electronic stability control on low friction collision avoidance in a truck driving simulator

Two experiments were carried out in a moving-base simulator, in which truck drivers of varying experience levels encountered a rear-end collision scenario on a low-friction road surface, with and without an electronic stability control (ESC) system. In the first experiment, the drivers experienced one instance of the rear-end scenario unexpectedly, and then several instances of a version of the scenario adapted for repeated collision avoidance. In the second experiment, the unexpected rear-end scenario concluded a stretch of driving otherwise unrelated to the study presented here. Across both experiments, novice drivers were found to collide more often than experienced drivers in the unexpected scenario. This result was found to be attributable mainly to longer steering reaction times of the novice drivers, possibly caused by lower expectancy for steering avoidance. The paradigm for repeated collision avoidance was able to reproduce the type of steering avoidance situation for which critical losses of control were observed in the unexpected scenario and, here, ESC was found to reliably reduce skidding and control loss. However, it remains unclear to what extent the results regarding ESC benefits in repeated avoidance are generalisable to unexpected situations. The approach of collecting data by appending one unexpected scenario to the end of an otherwise unrelated experiment was found useful, albeit with some caveats.

A simulation environment for analysis and optimization of driver models

A simulation environment for evaluation and optimization of driver models is introduced and described. The simulation environment features models of vehicles and drivers, as well as a representation of the traffic environment (roads, buildings etc.). In addition, an optimization framework based on stochastic optimization algorithms has been implemented as an integral part of the simulation environment. Given observed (time series) data of driver behavior and, possibly, vehicle dynamics, the optimization framework can be used for inferring driver model parameters. The simulation environment has been evaluated in two scenarios, one involving emergency braking and one involving a double lane change.

The Best Rated Human–Machine Interface Design for Autonomous Vehicles in the 2016 Grand Cooperative Driving Challenge

This paper provides an in-depth description of the best rated human-machine interface that was presented during the 2016 Grand Cooperative Driving Challenge. It was demonstrated by the Chalmers Truck Team as the envisioned interface to their open source software framework OpenDLV, which is used to power Chalmers’ fleet of self-driving vehicles. The design originates from the postulate that the vehicle is fully autonomous to handle even complex traffic scenarios. Thus, by including external and internal interfaces, and introducing a show, don’t tell principle, it aims at fulfilling the needs of the vehicle occupants as well as other participants in the traffic environment. The design also attempts to comply with, and slightly extend, the current traffic rules and legislation for the purpose of being realistic for full-scale implementation.

A Driver Model Using Optic Information for Longitudinal and Lateral Control of a Long Vehicle Combination

High driver acceptance is believed to be an important aspect when introducing automated driving functionalities for prospective long vehicle combinations. The main hypothesis of this paper is that high driver acceptance can be realized by utilizing driver models inspired by human cognition as an integrated part of such functions. It is envisioned that the human driver will more easily understand, and trust, a system that behaves in a human-like manner. In the study of a combined retardation and lane-change scenario, a driver model based on optic information was used, together with a single track vehicle model, to control the steering and retardation of a simulated vehicle. The parameters of the driver models lateral behavior were estimated using driving data measured from an A-double combination during actual lane-changes. Numerical simulations showed that the driver model was able to generate safe and conservative deceleration and steering for the studied scenario. In future work for automated functionalities, the combined driver and vehicle model could be used when evaluating different tentative plans for lane changes, in real time.

Systematic evaluation of sandboxed software deployment for real-time software on the example of a self-driving heavy vehicle

Companies developing and maintaining software-only products like web shops aim for establishing persistent links to their software running in the field. Monitoring data from real usage scenarios allows for a number of improvements in the software life-cycle, such as quick identification and solution of issues, and elicitation of requirements from previously unexpected usage. While the processes of continuous integration, continuous deployment, and continuous experimentation using sandboxing technologies are becoming well established in said software-only products, adopting similar practices for the automotive domain is more complex mainly due to real-time and safety constraints. In this paper, we systematically evaluate sandboxed software deployment in the context of a self-driving heavy vehicle that participated in the 2016 Grand Cooperative Driving Challenge (GCDC) in The Netherlands. We measured the systems scheduling precision after deploying applications in four different execution environments. Our results indicate that there is no significant difference in performance and overhead when sandboxed environments are used compared to natively deployed software. Thus, recent trends in software architecting, packaging, and maintenance using microservices encapsulated in sandboxes will help to realize similar software and system engineering for cyber-physical systems.

Containerized Development and Microservices for Self-Driving Vehicles: Experiences & Best Practices

In this paper, experiences and best practices from using containerized software microservices for self-driving vehicles are shared. We applied the containerized software paradigm successfully to both the software development and deployment to turn our software architecture in the vehicles following the idea of microservices. Key enabling elements include onboarding of new developers, both researchers and students, traceable development and packaging, convenient and bare-bone deployment, and traceably archiving binary distributions of our quickly evolving software environment. In this paper, we share our experience from working one year with containerized development and deployment for our self-driving vehicles highlighting our reflections and application-specific shortcomings, our approach uses several components from the widely used Docker ecosystem, but the discussion in this paper generalizes these concepts. We conclude that the growingly complex automotive software systems in combination with their computational platforms should be rather understood as data centers on wheels to design both, (a) the software development and deployment processes, and (b) the software architecture in such a way to enable continuous integration, continuous deployment, and continuous experimentation.