Establishing Trust in Vehicle-to-Vehicle Coordination: A Sensor Fusion Approach

As we add more autonomous and semi-autonomous vehicles (AVs) to our roads, their effects on passenger and pedestrian safety are becoming more important. Despite extensive testing, AVs do not always identify roadway hazards. Failures in object recognition components have already led to several fatal collisions, e.g. as a result of faults in sensors, software, or vantage point. Although a particular AV may fail, there is an untapped pool of information held by other AVs in the vicinity that could be used to identify roadway hazards before they present a safety threat

Establishing Trust in Vehicle-to-Vehicle Coordination: A Sensor Fusion Approach

Autonomous vehicles (AVs) use diverse sensors to understand their surroundings as they continually make safety- critical decisions. However, establishing trust with other AVs is a key prerequisite because safety-critical decisions cannot be made based on data shared from untrusted sources. Existing protocols require an infrastructure network connection and a third-party root of trust to establish a secure channel, which are not always available. In this paper, we propose a sensor-fusion approach for mobile trust establishment, which combines GPS and visual data. The combined data forms evidence that one vehicle is nearby another, which is a strong indication that it is not a remote adversary hence trustworthy. Our preliminary experiments show that our sensor-fusion approach achieves above 80% successful pairing of two legitimate vehicles observing the same object with 5 meters of error. Based on these preliminary results, we anticipate that a refined approach can support fuzzy trust establishment, enabling better collaboration between nearby AVs

Establishing Trust in Vehicle-to-Vehicle Coordination: A Sensor Fusion Approach

As we add more autonomous and semi-autonomous vehicles (AVs) to our roads, their effects on passenger and pedestrian safety are becoming more important. Despite extensive testing, AVs do not always identify roadway hazards. Failures in object recognition components have already led to several fatal collisions, e.g. as a result of faults in sensors, software, or vantage point. Although a particular AV may fail, there is an untapped pool of information held by other AVs in the vicinity that could be used to identify roadway hazards before they present a safety threat