Jonas Bärgman

Bio: Jonas Bärgman is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Crash & Poison control. The author has an hindex of 16, co-authored 53 publications receiving 922 citations. Previous affiliations of Jonas Bärgman include Autoliv & University of Michigan.

Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk

Abstract: This work was sponsored by the second Strategic Highway Research Program (SHRP 2), which is administered by the Transportation Research Board of the National Academies. This project was managed by Ken Campbell, Chief Program Officer for SHRP 2 Safety , and Jim Hedlund, SHRP 2 Safety Coordinator . The research reported on herein was performed by the main contractor SAFER Vehicle and Traffic Safety Centre at Chalmers, Gothenburg, Sweden. SAFER is a joint research unit where 25 partners from the Swedish automotive industry, academia and authorities cooperate to make a center of excellence within the field of vehicle and traffic safety (see www.chalmers.se/safer ). The host and legal entity SAFER is Chalmers University of Technology. Principle Investigator Tr ent Victor is Adjunct Professor at Chalmers and worked on the project as borrowed personnel to Chalmers but his main employer is Volvo Cars. The other authors of this report are Co - PI Marco Dozza, Jonas Bargman, and Christian - Nils Boda of Chalmers Universi ty of Technology (as a SAFER partner) ; Johan Engstrom and Gustav Markkula of Volvo Group Trucks Technology (as a SAFER partner) ; John D. Lee of University of Wisconsin - Madison (as a consultant to SAFER); and Carol Flannagan of University of Michigan Transp ortation Research Institute (UMTRI) (as a consultant to SAFER). The authors acknowledge the contributions to this research from Ines Heinig, Vera Lisovskaja, Olle Nerman, Holger Rootzen, Dmitrii Zholud, Helena Gellerman , Leyla Vujic, Martin Rensfeldt, Stefan Venbrant, Akhil Krishnan, Bharat Mohan Redrouthu, Daniel Nilsson of Chalmers; Mikael Ljung - Aust of Volvo Cars; Erwin Boer; Christer Ahlstrom and Omar Bagdadi of VTI.

Vulnerable road users and the coming wave of automated vehicles : Expert perspectives

Abstract: Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.

How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2

Abstract: As naturalistic driving data become increasingly available, new analyses are revealing the significance of drivers’ glance behavior in traffic crashes. Due to the rarity of crashes, even in the largest naturalistic datasets, near-crashes are often included in the analyses and used as surrogates for crashes. However, to date we lack a method to assess the extent to which driver glance behavior influences crash and injury risk across both crashes and near-crashes. This paper presents a novel method for estimating crash and injury risk from off-road glance behavior for crashes and near-crashes alike; this method can also be used to evaluate the safety impact of secondary tasks (such as tuning the radio). We apply a ‘what-if’ (counterfactual) simulation to 37 lead-vehicle crashes and 186 lead-vehicle near-crashes from lead-vehicle scenarios identified in the SHRP2 naturalistic driving data. The simulation combines the kinematics of the two conflicting vehicles with a model of driver glance behavior to estimate two probabilities: (1) that each event becomes a crash, and (2) that each event causes a specific level of injury. The usefulness of the method is demonstrated by comparing the crash and injury risk of normal driving with the risks of driving while performing one of three secondary tasks: the Rockwell radio-tuning task and two hypothetical tasks. Alternative applications of the method and its metrics are also discussed. The method presented in this paper can guide the design of safer driver–vehicle interfaces by showing the best tradeoff between the percent of glances that are on-road, the distribution of off-road glances, and the total task time for different tasks.

Great expectations: A predictive processing account of automobile driving

Abstract: Predictive processing has been proposed as a unifying framework for understanding brain function, suggesting that cognition and behaviour can be fundamentally understood based on the single principle of prediction error minimisation. According to predictive processing, the brain is a statistical organ that continuously attempts get a grip on states in the world by predicting how these states cause sensory input and minimising the deviations between the predicted and actual input. While these ideas have had a strong influence in neuroscience and cognitive science, they have so far not been adopted in applied human factors research. The present paper represents a first attempt to do so, exploring how predictive processing concepts can be used to understand automobile driving. It is shown how a framework based on predictive processing may provide a novel perspective on a range of driving phenomena and offer a unifying framework for traditionally disparate human factors models.

System and method for predicting behaviors of detected objects

Abstract: Aspects of the invention relate generally to autonomous vehicles. Specifically, the features described may be used alone or in combination in order to improve the safety, use, driver experience, and performance of these vehicles.

Construction zone object detection using light detection and ranging

Abstract: Methods and systems for construction zone object detection are described. A computing device may be configured to receive, from a LIDAR, a 3D point cloud of a road on which a vehicle is travelling. The 3D point cloud may comprise points corresponding to light reflected from objects on the road. Also, the computing device may be configured to determine sets of points in the 3D point cloud representing an area within a threshold distance from a surface of the road. Further, the computing device may be configured to identify construction zone objects in the sets of points. Further, the computing device may be configured to determine a likelihood of existence of a construction zone, based on the identification. Based on the likelihood, the computing device may be configured to modify a control strategy of the vehicle; and control the vehicle based on the modified control strategy.

Vehicle rollover detection system

Abstract: A roll angular velocity sensor (20) and an occupant sensor (42, 56.1, 56.2, 60.1, 60.2) are operatively coupled to a processor (26), which provides for detecting a rollover condition responsive to a measure of roll angular velocity and controlling a safety restraint system (30, 30.1, 30.2, 32.1, 32.2, 36.1, 36.2, 38.1, 38.2, 39.1, 39.2, 40.1, 40.2) responsive thereto, wherein a detection criteria associated with the rollover detection process is responsive to a signal from the occupant sensor. In one embodiment, a closure time is estimated from estimates or measurements of occupant velocity or acceleration, and the estimated closure time is compared with a threshold. If the estimated closure time is less than the threshold, activation of the safety restraint system is either inhibited or advanced relative to that otherwise provided by the rollover detection process alone. Otherwise, the activation may be delayed to provide additional time for the rollover detection.

Driving pattern recognition and safety control

Abstract: Systems and methods are provided for controlling a vehicle A safe envelope driving pattern is determined to control the vehicle in an autonomous mode User identification data and sensor data are received from one or more sensors associated with the vehicle A driver-specific driving pattern is determined based on the received sensor data and the user identification data Operation of the vehicle is controlled in the autonomous mode based on the identification of the user in the driver's seat, the safe envelope driving pattern, and the user-specific driving pattern

Determining when to drive autonomously

Abstract: Aspects of the disclosure relate generally to determining whether an autonomous vehicle should be driven in an autonomous or semiautonomous mode (where steering, acceleration, and braking are controlled by the vehicle's computer). For example, a computer may maneuver a vehicle in an autonomous or a semiautonomous mode. The computer may continuously receive data from one or more sensors. This data may be processed to identify objects and the characteristics of the objects. The detected objects and their respective characteristics may be compared to a traffic pattern model and detailed map information. If the characteristics of the objects deviate from the traffic pattern model or detailed map information by more than some acceptable deviation threshold value, the computer may generate an alert to inform the driver of the need to take control of the vehicle or the computer may maneuver the vehicle in order to avoid any problems.