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A Bayesian Framework for Preventive Assistance at Road Intersections


Modern vehicles embed an increasing number of Advanced Driving Assistance Systems (ADAS). Whilst such systems showed their capability to improve comfort and safety, most of them provide assistance only as a last resort, that is, they alert the driver or trigger automatic braking only when collision is imminent. This limitation is mainly due to the difficulty to accurately anticipate risk situations in order to provide the driver with preventive assistance, i.e. assistance allowing for comfortable reaction. This paper presents a Bayesian framework which aims to detect risk situations sufficiently early to trigger conventional curative assistance as well as preventive assistance. By taking into consideration the context, the vehicle state, the driver actuation and the manner how the driver usually negotiates given situations, the framework allows to infer which type of assistance is the most pertinent to be provided to the driver. The principles of this framework are applied to a fundamental case study, the arrival to a stop intersection. Results obtained from data recorded under controlled conditions are presented. They show that the framework allows to coherently detect risk situations and to identify what assistance, including preventive assistance, is the most appropriate for the situation.
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hal-01319046 , version 1 (23-05-2016)


  • HAL Id : hal-01319046 , version 1


Alexandre Armand, David Filliat, Javier Ibañez-Guzman. A Bayesian Framework for Preventive Assistance at Road Intersections. 2016 IEEE Intelligent Vehicles Symposium (IV'16), Jun 2016, Göteborg, Sweden. ⟨hal-01319046⟩
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