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Driving while fatigued is dangerous and may result in car accidents. Moreover, adding technology to assist driving can distract the driver. In this work, we present a noninvasive and non-distracting method for monitoring driver emotions and fatigue. To the best of our knowledge, no scientifically agreed definition of emotions exists, as a physical state to be monitored. Emotion recognition is defined as measuring observations of motor system behavior that correspond with high probability to an underlying emotion or combination of emotions. This definition is based on the fact that measuring cognitive influences is currently impossible. In this work we have investigated driving sessions for identifying possible risks at drive in a certain itinerary, analyzing HRV trend. In general, HRV signals can be used as indicators of the responses of the autonomic nervous system (ANS) because the ANS is influenced by the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). The first one (SNS) response to an alarm situation: struggle, stress, drowsiness (and other factors) while PNS, when is activated, produces a slowing down of heart rate and breathing therefore PNS is the normal response to a situation of calmness and absence of danger and stress. For the test, we have collected ECG signals (Electrocardiography) and driver position in 15 trips on 2 daily sessions, by 2 different drivers on the same path. Results show an interesting geospatial relationship of emotions on the travelled path

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