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While the car ownership increasing all over the world, the unutilized thermal energy in automobile exhaust system is gradually being realized and valued by researchers around the world for better driving energy efficiency. For the unexpected urban traffic, the frequent start and stop processes as well as the acceleration and deceleration lead to the temperature fluctuation of the exhaust gas, which means the unstable hot-end temperature of the thermoelectric module generator (TEG). By arranging the heat conduction oil circulation at the hot end, the hot-end temperature’s fluctuation of the TEG can be effectively reduced, at the expense of larger system size and additional energy supply for the circulation. This research improves the TEG hot-end temperature stability by installing solid heat capacity material(SHCM) to the area between the outer wall of the exhaust pipe and the TEG, which has the merits of simple structure, none energy consumption and light weight.

The mountainous roads are rugged and complex, so that the driver can not make accurate judgments on dangerous road conditions. In addition, most heavy vehicles have characteristics of large weight and high center of gravity. The two factors above have caused most of the car accidents in mountain areas. A research shows that 90% of car accidents can be avoided if drivers can respond within 2-3 seconds before the accidents happen. This paper proposes a speed warning scheme for heavy-duty vehicle over the horizon in mountainous area, which can give the drivers enough time to respond to the danger. In the early warning aspect, this system combines the front road information, the vehicle characteristics and real-time information obtained from the vehicle, calculates and forecasts the danger that may happen over the horizon ahead of time, and prompts the driver to control the vehicle speed.

The development of the vehicle quantity and the transportation system accompanies the rise of traffic accidents. Statistics shows that nearly 35-45% traffic accidents are due to drivers’ fatigue. If the driver’s fatigue status could be judged in advance and reminded accurately, the driving safety could be further improved. In this research, the blink frequency and eyes movement information are monitored and the statistical method was used to assess the status of the driving fatigue. The main tasks include locating the edge of the human eyes, obtaining the distance between the upper and lower eyelids for calculating the frequency of the driver's blink. The velocity and position of eyes movement are calculated by detecting the pupils’ movement. The normal eyes movement model is established and the corresponding database is updated constantly by monitoring the driver blink frequency and eyes movement during a certain period of time.